Bearing Defect Signature Analysis Using Advanced Nonlinear

NASA Technical Memorandum 108491

Bearing Defect Signature Analysis Using AdvancedNonlinear Signal Analysis in a Controlled EnvironmentCDDF Final Report, Project No. 93-10

T. Zoladz, E. Earhart, and T. Fiorucci

Marshall Space Flight Center • MSFC, Alabama

National Aeronautics and Space AdministrationMarshall Space Flight Center ° MSFC, Alabama 35812

May 1995

https://ntrs.nasa.gov/search.jsp?R=19950021943 2018-03-24T22:01:34+00:00Z

TABLE OF CONTENTS

I. INTRODUCTION ................................................................................................................

II. TEST SETUP .......................................................................................................................

III. BEARING FAULT PATTERNS AND ANALYSIS METHODS ....................................

A. Bearing Fault Patterns ..................................................................................................

B. Analysis Methods ..........................................................................................................

IV. ANALYSIS RESULTS ........................................................................................................

A. Conventional PSD Analysis Results ............................................................................

B. Nonlinear/Bispectral Analysis Results, 0- to 5-kHz Data .........................................

C. Envelope Detection Analysis Results (Accelerometer Data) ....................................

D. Envelope Detection Analysis Results (Acoustic Emission Data) ............................E. Postenvelope Bispectral Analysis Results ..................................................................

V. SSME TURBOPUMP BEARING EXAMPLE ..................................................................

VI. CONCLUSIONS/RECOMMENDATIONS .......................................................................

REFERENCES ..............................................................................................................................

APPENDIX - ROLLER BEARING FREQUENCY CALCULATIONS ....................................

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LIST OF ILLUSTRATIONS

Figure

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3(a).

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4(a).

4(b).

4(c).

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Title

Rotor assembly test article for CDDF-sponsored research ....................................

Analytically predicted beating defect vibration spectrum(McFadden and Smith'* s) ..........................................................................................

Conventional 5-kHz acceleration spectra across several test bearing

configurations ..............................................................................................................

Bearing block 90* location accelerometer spectrum for good roller bearing .............

Bearing block 90* location accelerometer spectrum for roller bearing withinner race defect ..........................................................................................................

Bearing block 90" location accelerometer spectrum for roller bearing with

rolling element defect ..................................................................................................

Bearing block 270 ° location accelerometer spectrum for good roller bearing ...........

Bearing block 270 ° location accelerometer spectrum for roller beating withinner race defect ..........................................................................................................

Bearing block 270" location accelerometer spectrum for roller beating with


Raw acceleration (0- to 5-kHz band limited) waveforms across several

test bearing configurations .........................................................................................

Bearing block 90* location accelerometer waveform for good roller beating ............

Bearing block 90* location accelerometer waveform for roller bearing withinner race defect ..........................................................................................................

Bearing block 90" location accelerometer waveform for roller bearing with


Bearing block 270" location accelerometer waveform for good roller beating ..........

Bearing block 270" location accelerometer waveform for roller bearing withinner race defect ..........................................................................................................

Bearing block 270" location accelerometer waveform for roller bearing with


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LIST OF ILLUSTRATIONS (Continued)

Figure

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5(a).

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7(b).

7(c).

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Title

Roller beating inner race defect bicoherence estimation using beating block90" location accelerometer data (0 to 5 kHz) for a reference frequency set at

inner race roller passing impact rate ..........................................................................

0- to 5-kHz PSD for bearing block 90 ° location accelerometer ................................

0- to 5-kHz bicoherence spectrum corresponding to PSD of figure 5(a),

reference frequency set at inner race roller passing impact rate ..............................

Roller bearing rolling element defect bicoherence estimation using beating

block 90 ° location accelerometer data (0 to 5 kHz) for a reference frequency

set at rolling element spin frequency .........................................................................

0- to 5-kHz PSD for bearing block 90 ° location accelerometer ................................


reference frequency set at rolling element spin frequency ........................................

Wideband acceleration spectra across several test bearing configurations ...........

Beating block 90* location accelerometer spectrum for good roller beating .............

Beating block 90" location accelerometer spectrum for roller bearing withinner race defect ..........................................................................................................

Bearing block 90 ° location accelerometer spectrum for roller beating withroiling element defect ..................................................................................................

Bearing block 270 ° location accelerometer spectrum for good roller bearing ...........

Bearing block 270 ° location accelerometer spectrum for roller bearing withinner race defect ..........................................................................................................

Bearing block 270 ° location accelerometer spectrum for roller bearing withrolling element defect ..................................................................................................

Envelope spectra derived from wideband acceleration signals across severaltest beating configurations ........................................................................................

Beating block 90 ° location accelerometer envelope recovered spectrum forgood roller bearing ......................................................................................................

Bearing block 90" location accelerometer envelope recovered spectrum forroller bearing with inner race defect ...........................................................................

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Figure

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Title

Bearing block 90* location accelerometer envelope recovered spectrum for

roller bearing with rolling element defect ..................................................................

Bearing block 270 ° location accelerometer envelope recovered spectrum for

good roller beating ......................................................................................................

Bearing block 270 ° location accelerometer envelope recovered spectrum forroller beating with inner race defect ...........................................................................

Bearing block 270* location accelerometer envelope recovered spectrum for

roller bearing with rolling element defect ..................................................................

Wideband acceleration waveforms across several test beating configurations .....

Bearing block 90" location accelerometer wideband waveform for

good roller bearing ......................................................................................................

Bearing block 90" location accelerometer wideband waveform for roller

bearing with inner race defect ....................................................................................


bearing with rolling element defect ............................................................................

Beating block 270" location accelerometer wideband waveform for

good roller bearing ......................................................................................................

Bearing block 270 ° location accelerometer wideband waveform forroller bearing with inner race defect ...........................................................................


bearing with rolling element defect ............................................................................

Wideband acoustic emission spectra across several test bearing

configurations ..............................................................................................................

Wideband acoustic emission spectrum for good roller bearing ................................

Wideband acoustic emission spectrum for roller beating with innerrace defect ...................................................................................................................

Wideband acoustic emission spectrum for roller bearing with rollingelement defect .............................................................................................................

Envelope spectra derived from acoustic emissions across several test

beating configurations .................................................................................................

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Figure

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Title

Acoustic emission envelope recovered spectrum for good roller bearing ................

Acoustic emission envelope recovered spectrum for roller bearing withinner race defect ..........................................................................................................

Acoustic emission envelope recovered spectrum for roller bearing withrolling element defect ..................................................................................................

Acoustic emission waveforms across several test bearing configurations .............

Acoustic emissions from good roller bearing ............................................................

Acoustic emissions from roller bearing with inner race defect .................................

Acoustic emissions from roller bearing with rolling element defect ........................

Comparison of envelope recovered spectra to conventional vibration PSD

and analytically predicted vibration spectrum, roller bearing inner racedefect case ...................................................................................................................

Bearing block 90 ° location 0- to 5-kHz acceleration spectrum for rollerbearing with inner race defect ....................................................................................

Bearing block 90 ° location 0- to 5-kHz envelope recovered spectrum forroller bearing with inner race defect ...........................................................................

Acoustic emission envelope recovered spectrum for roller bearing withinner race defect ..........................................................................................................

Analytically predicted bearing defect vibration spectrum(McFadden and Smith 4) .............................................................................................

Comparison of wideband and envelope bearing defect waveforms toconventional (0- to 5-kHz) acceleration waveform for inner race defect case .......

Bearing block 90 ° accelerometer low-frequency (0- to 5-kHz) response forroller bearing with inner race defect ...........................................................................

Bearing block 90 ° accelerometer wideband (0- to 130-kHz) waveform for

roller bearing with inner race defect ...........................................................................

Acoustic emission response (50 to 400 kHz) for roller bearing with innerrace defect ...................................................................................................................

Envelope of waveform shown in figure 14(c) ............................................................

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Figure Title Page

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Roller bearing inner race defect bicoherence estimation using envelope of

bearing block 90" accelerometer data, reference frequency set at inner

race roller passing impact rate ...................................................................................

0- to 5-kHz PSD of bearing block 90 ° accelerometer wideband response

envelope ......................................................................................................................



Roller bearing inner race defect bicoherence estimation using envelopeof acoustic emissions, reference frequency set at inner race roller

passing impact rate .....................................................................................................

0- to 5-kHz PSD of acoustic emission wideband response envelope ....................



Roller bearing rolling element defect bicoherence estimation using envelope

of bearing block 90* accelerometer data, reference frequency set at rolling

element spin frequency ...............................................................................................

0- to 5-kHz PSD of bearing block 90 ° accelerometer wideband response

envelope ......................................................................................................................



Roller bearing rolling element defect bicoherence estimation using envelopeof acoustic emissions, reference frequency set at rolling element spin

frequency .....................................................................................................................

0- to 5-kHz PSD of acoustic emission wideband response envelope ....................



High-frequency characterization of SSME alternate HPOTP vibration

immediately prior to pump-end ball bearing degradation incident ...........................

Pump-end accelerometer 0- to 25-kHz conventional PSD ......................................

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Vlll


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Title

Pump-end accelerometer 0- to 5-kHz conventional PSD ........................................

Pump-end accelerometer 0- to 5-kHz envelope PSD ..............................................

High-frequency (15- to 25-kHz) waveform from pump-end accelerometer

during PSD estimation times of figures 19(a), (b), (c) ............................................

High-frequency characterization of SSME alternate HPOTP vibration

during pump-end ball bearing degradation incident ..................................................

Pump-end accelerometer 0- to 25-kHz conventional PSD ......................................

Pump-end accelerometer 0- to 5-kHz conventional PSD ........................................

Pump-end accelerometer 0- to 5-kHz envelope PSD ..............................................

High-frequency (15- to 25-kHz) waveform from pump-end accelerometer

during PSD estimation times of figures 20(a), (b), (c) ............................................

ATD HPOTP bicoherence estimation using envelope of wideband

pump-end accelerometer data, reference frequency set at pump-endball bearing ball spin frequency ..................................................................................

0- to 5-kHz PSD of pump-end accelerometer wideband response envelope .........

0- to 5-kHz bicoherence spectrum corresponding to PSD of 21(a), reference

frequency set at pump-end ball bearing ball spin frequency ...................................

Joint time/frequency mapping of SSME alternate HPOTP pump-end

accelerometer recovered envelope signal during pump-end ball bearing

degradation incident hot-fire test with ball bearing coolant circuit

delta-temperature overlay .........................................................................................

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ABBREVIATIONS AND ACRONYMS

A/D

ABC

ABS

AE

ATD

BS

C

CDDF

HPOTP

IRIG

IRP

N

PSD

RPM

RS

SSME

TN

analog-to-digital

autobicoherence

autobispectrum

acoustic emission

alternate design (SSME)

ball spin frequency (Hz)

roller/ball train cage frequency (Hz)

Center Director's Discretionary Fund

high pressure oxygen turbopump (SSME)

Inter-Range Instrumentation Group

inner race defect passing frequency (Hz)

rotor synchronous speed (Hz)

power spectral density

rounds per minute

rolling element spin frequency (Hz)

space shuttle main engine

fundamental period of rotor shaft rotation (s)

f_,C.f_Dli_l_ _ _.,_d'f _ xi

TECHNICAL MEMORANDUM

BEARING DEFECT SIGNATURE ANALYSIS USING ADVANCED NONLINEAR

SIGNAL ANALYSIS IN A CONTROLLED ENVIRONMENT

CDDF FINAL REPORT (NO. 93-10)

I. INTRODUCTION

Accurate machinery fault detection and diagnosis have always been significant technical

challenges in the aeronautics and transportation industries. Within the aerospace industry, the reli-

able health monitoring of propulsion systems is a necessity in preventing catastrophic system

failures and costly engine down time due to false alarms. Since machines "talk" through their

sounds and vibrations, an analyst, given the appropriate set of tools, can listen to their complaints

and diagnose their ailments. The signs and symptoms of these ailments appear as subtle complex

dynamic signatures hidden deep within random signals sensed through high-frequency instrumenta-

tion mounted externally to the ;machine. However, these symptoms are often confused with other

responses emitted by the monitored machine such as rotor dynamic, structural, and environmental

operational elements, along with electronic line noise contributed by the sensor signal conditioning

equipment. Ultimately, the success of a machinery fault detection and isolation program relies on itsability to first extract these complex dynamic signals from random data, then to form an accurate

interpretation and diagnosis consistent with these "complaints" from the machine.

During the development of the space shuttle main engine (SSME), significant progress has

been made within both NASA and the aerospace communities toward improving the machinery fault

diagnostic function through research in instrumentation, modeling, and dynamic signal analysis. This

research has significantly enhanced the safety of space shuttle operations. Within the Structures and

Dynamics Laboratory of Marshall Space Flight Center, major progress has been made through the

development of a hierarchy of advanced nonlinear signal spectral analysis techniques for mechanical

signature analysis. The research has progressively led to the introduction of new signal processingtechniques which can identify intelligent machine operating state information hidden deep within theextraneous corruptive noise of dynamic measurements, information often unidentifiable using con-

ventional signal analysis methods. By providing additional insight into the monitored system

response, these "tools" allow better identification of well-hidden defect symptoms such as beating

flaws within SSME high-speed turbopumps. Moreover, by using phase information inherent in these

signals, the nonlinear analysis methods separate false-alarm, i.e., benign, signatures from true

defect signals allowing hardware integrity to be maintained.

This document summarizes a Center Director's Discretionary Fund (CDDF) research effort

that investigated rolling element bearing fault dynamic signatures. The sponsored research used

both traditional and state-of-the art (acoustic emission (AE)) sensors and signal-processing

techniques in the characterization of high-frequency data from a laboratory rotor system seededwith bearing faults. The research concentrated on applying nonlinear signal analysis techniques

which identify hidden relationships between multiple spectral components within and across high-

frequency monitoring channels in diagnosing implanted bearing faults. The coupling of these nonlinear

signal analysis "tools" with AE sensor technology was the innovative focus of this research.

II. TEST SETUP

This research effort utilized a highly instrumented rotor assembly as a test article (fig. 1).

Since the commercially available rotor rig was not fitted with off-the-shelf rolling element beatings,

it was modified with a dual beating block which allowed for quick beating change-out to expedite thebearing defect data gathering and analysis process. Due to this bearing retrofit process, a rotor

dynamic analysis was performed on the assembly to determine if any rotor modes would interfere

with the beating defect analysis. The analysis concluded that no rotor modes would be excited in the

operating range of the assembly (0 to 10,000 r/min) during testing.

test bearing block

(instrumented with accels, velocity transducers, AE probe)

drive motor

key pbaser outX and Y-axis proximity out

side load application

cinder block/carpet isolation mount _

Figure 1. Rotor assembly test article for CDDF-sponsored research.

In this study, both ball and roller type rolling element beatings were tested. An 8-ball, deep-

groove, ball beating manufactured by SKF, model number 6202 JEM, and an 11-roller cylindrical

roller bearing also manufactured by SKF, model number NU 202 ECP, were chosen as test articles

for the research. The appendix contains calculations for the characteristic bearing frequencies, as

ratios to rotor speed, for the two test bearings. These frequencies, which include cage (ball/rollertrain), outer rolling element passing, inner rolling element passing, and rolling element spin, are

calculated using simple well-documented formulas which are a function of ball/roller diameter, pitch

diameter, rolling element contact angle, and number of rolling elements. 1

Conventional vibration sensors including accelerometers, velocity probes, and proximitors

were mounted at various locations on the rotor/beating block assembly. Along with these traditional

high-frequency sensors, one AE probe was affixed to the test bearing block during all testing. Use of

AE technology for beating defect analysis is comparatively recent. 2 Use of an AE type sensor in

bearing fault detection is very promising since it provides immunity from structural, rotor dynamic,

and environmental noise that floods conventional sensors. Use of the AE sensor allows subsequent

analysis to focus on the bearing condition. AE sensors are designed to detect energy traveling asLamb waves from defect source to sensor, with the wave front passing through the structure at its

shear wave velocity. It is the elastic propagation of these waves in the material media that consti-

tutes the AE energy/signals. These bursts of emissions are typically sensed in the 100- to

1,000-kHz region. 3 These AE sensor response regions typically extend much higher in frequency

than conventional mechanical signature analysis bandwidths using traditional transducers, i.e.,

accelerometers, which seldom exceed 20 kHz. However, along with the expected benefits associated

with the AE sensor's higher-frequency response come challenging analog-to-digital (A/D)

2

conversionrequirements.In this CDDF analysis,a PhysicalAcousticsCorp. model $9208AEsensorwasusedto collect emissionsfrom the bearingtestarticles through the bearingsupportblock. Sincethe AE probehad broadbandsensitivity extendingout past 1.25MHz, the channelwasrecordedin a direct recordingmodein parallelwith theremainingconventionalsensorchannelsin afrequencymodulating(FM) recordingmode.Directmoderecordingof theAE channelallowedfor ananalogrecordingbandwidthof 400 Hz to 2 MHz, while theFM channelscapturedsignal input in therangefrom 0 Hz to 40kHz. This FM modewassufficient for theconventionallower-frequencyresponseaccelerometers,velocity probes,andproximitors. Due to its high-frequencyresponse,theAE channelwasdigitized separatelyduring MD processing. In fact, in order for the existinglaboratory A/D system to successfully sample and log the acquired data, the replay tape speed of the

analog recorder had to be reduced from 120 to 3.75 in/s, a factor of 32. In order to synchronize the AE

data with the conventional low-frequency data recorded on the FM channels, an IRIG time code

signal was concurrently digitized with all sensor data.

As previously mentioned, both roller and ball type rolling element beatings were used as test

articles during the investigation. In an effort to simulate early spalling damage, single-point seededfaults were implanted in the bearings in the form of fine linear axial (relative rotor shaft) scratches in

the rolling elements themselves and their raceways. The bearings were then mounted on sleeves,

affixed to the rotor shaft, and placed into the bearing block with contact between the bearing outer

races and the block preventing rotation of their outer raceways. Several bearing defect scenarios

were tested during this CDDF study, however, only three are presented in this report:

• Single-roller type bearing with no imbedded faults

• Single-roller type bearing with small axially extending scratch imbedded in inner raceway

• Single-roller type bearing with small axially extending scratch imbedded in one rollingelement.

During each of the bearing tests, rotor speed was linearly increased from idle to 10,050 r/min

(168 Hz) where it dwelled for several seconds and then returned back to idle. Typically, test

durations were approximately 140 s. During testing, selected data channels were monitored ondynamic signal analyzers and oscilloscope for test control.

III. BEARING FAULT PATTERNS AND ANALYSIS METHODS

A. Bearing Fault Patterns

Faults in bearings tend to generate characteristic frequencies which can be invaluable in

diagnosing their health. Even though the computation of fundamental characteristic bearing frequen-

cies is straightforward, other factors such as modulation effects can complicate the bearing defectvibration signature. McFadden and Smith 4 5 give an excellent discussion of the vibration signatures

produced by both single- and multiple-point defects within rolling element beatings. In their analytic

model, the sensed bearing defect vibration waveform is primarily determined by bearing geometry

(which allows for calculation of impact rates), bearing load distribution, and the transmission charac-

teristics of the bearing defect energy from the point of impact to the sensing location. Figure 2 showsthe general form of the analytically predicted envelope spectrum for the vibration emitted by a bear-

ing with a single-point defect.

/!

I

0

Envelope due to exponentiol decoy

_zr, /II I

i i

i II #

iiII

- ,P I iI M

i

1t_ • V

Main Iol=,es

.

I

!, 1

,, rlt I

i I

I;

, tSide-lobes

:_f

Figure 2. Analytically predicted bearing defect vibration spectrum. 4 5

The term envelope indicates that the spectrum was recovered using a high-frequencyresonance, also referred to as envelope detection, technique. If the periodic impacts generated

by a defective beating surface making contact with another bearing surface excite, i.e., "ring,"

high-frequency beating/machine structural or transducer resonances, the character of the beating

vibration can be recovered from the excited resonant band of energy. Of interest in envelope analysis

is not what system or transducer resonance is excited (the carrier), but the impulse repetition

frequency (the envelope) which allows characterization of the bearing fault. Given optimal

transmissibility between source and sensor in the frequency band containing the characteristicbeating frequencies themselves, the spectral pattern shpwn in figure 2 could be recovered using

conventional signal analysis techniques without envelope-type analysis. However, in real-lifemachinery monitoring situations, these beating defect components are frequently highly damped

since they are sensed externally on the machine housing. Moreover, if they are apparent in the low-

frequency region of the vibration signal, they are commonly confused with other rotor dynamic,hydrodynamic, and environmental noise sources that tend to confound the signal spectrum. In this

CDDF study, nonlinear spectral analysis was applied to such confusing multiple source signals to

determine if bearing defect signatures could be separated from unrelated "noise" without relying on

envelope detection processing. Later, these results were compared to envelope spectral analysis

results using both accelerometer and AE-sensed signals. Finally, nonlinear spectral analysis was

applied to recovered envelope signals to further enhance bearing defect signature retrieval results

and to verify the analytically predicted nonlinear complex modulation sideband structures shown infigure 2.

As previously mentioned, bearing defect vibration character is dictated by beating geometry,

bearing load, and defect energy transmission characteristics. The analytic bearing fault pattern of

figure 2 consists of clusters or groups of discrete frequencies with members in each successive group

separated by a frequency represented by 'f,," the modulating frequency, and the center-most peak

of each group spaced in frequency by '_fd," the carrier frequency. A beating with a single inner race

defect would exhibit a modulating frequency equal to shaft rotational speed and a carder frequency

corresponding to its inner race element passing frequency. On the other hand, a beating with a singlerolling element defect would exhibit a modulating frequency equivalent to its cage rotational

frequency and a carder frequency of rolling element spin frequency. The lobing character seen in the

progressive groups of spectral components in figure 2 is due to the bearing load cycle which

4

influencesthe bearingsurfaceimpactintensity.The largestcomponentin eachof the successiveclustersis the elementpassingor spin frequency.The generaldecayin amplitudeof the successivegroupsof frequenciesis attributedto thedampingof the defectimpulsesduring wavepropagationfrom sourceto sensor.

B. Analysis Methods

1. Conventional Power Spectral Density Analy_i_. Conventional bearing diagnostic evalua-

tion is done by assessing the power spectral density (PSD) content of monitored high-frequencybearing/machine instrumentation channels. Baseline response, i.e., PSD amplitude levels, at charac-

teristic bearing frequencies for given machine instrumentation locations are developed and archived

for trend analysis with newly acquired data. Such analysis is beneficial to a machinery health moni-toring program but can be somewhat limited as a bearing health diagnostic tool in several instances.

For instance, in several machinery systems, externally sensed bearing vibrations are highly damped,

and, by the time bearing characteristic frequencies are sensed on the machine housing, significant

bearing degradation has already occurred. In other instances, complex spectral content due to

machinery rotor dynamic, hydrodynamic, or environmental responses resembles the bearing frequen-cies (fault patterns) being monitored and confuses the bearing health diagnosis. The biggest short-

coming with linear spectral analysis anchored bearing health analysis is its oversight of pertinent

intra- and cross-channel phase relationships existing between the many spectral components asso-

ciated with complex bearing vibration waveforms. This limitation of the conventional PSD analysis iswhat motivated the development of several advanced nonlinear signal analysis tools within the

Structures and Dynamics Laboratory over the past decade for propulsion system turbomachineryanalysis.

2. Nonlinear/Bispectral Analy_i_. Nonlinear spectral analysis techniques improve bearing

vibration signature analysis by exploiting hidden nonlinear phase relationships within the spectra ofacquired high-frequency diagnostic data. 6-8 As seen in the complex defective bearing spectrum

shown in figure 2, fundamental bearing signatures consist of key harmonic and modulation sideband

structures. However, these patterns are not always easily identifiable. Where PSD analysis reliesonly on identification of the frequency spacings in such patterns, nonlinear analysis identifies the

inherent phase coupling existing between individual members of such complex frequency structures.

As the PSD function is a second moment statistic of a random signal, the auto-bispectrum

(ABS) represents the third joint moment among three different waves, spectral components, at fre-

quencies 091, ca2, and the sum frequency 091+0)2, and can be estimated by:

Bxxx (09,,092) = E[X(09,)X(092)X'(o), + co2 )] , (1)

where X(09) is the Fourier transform of the monitored instrumentation channel, x(t). The auto-bicoherence (ABC), a normalized bispectrum, is defined as:

(col,co2)= I' x (col,o )12E[I X (09_) X(092)12 ] E[IX (to_ + c02)12 ]

(2)

The ABS Bxxx (091,092) is a function of two independent frequencies, 091 and 092 (along with an implicit

third wave of frequency oh +o92), rather than a single-frequency argument as with a PSD. Therefore, a

three-dimensionalfigure would be required to display a bispectrum since it is a function of two fre-

quency arguments. However, to maximize the visualization effect, one of the bi-frequency argu-

ments, c01, can be fixed at some particular frequency of interest, such as a bearing characteristic

frequency, while the other frequency argument, c92, sweeps through the entire analysis frequency

range of the input signal.

Analytically, auto/cross bicoherence can be shown bounded by zero and unity. Since basicbearing defect mechanisms produce amplitude modulated vibrations involving three basic spectral

components at frequencies oh, r.02, and their sum frequency, oh +a,_2, bicoherence is a perfect

candidate for beating diagnostic applications. Even though a simple PSD might be able to identify the

power distribution at these three particular bearing-related frequencies, the existence of modulationcan only be proven by identifying/extracting the coherent phase relationship among the three fre-

quency components. The phase information of the bispectrum in equation (1) provides a unique tool

for identifying such phase coupling. If the wave at frequency oh +a_2, is perfectly correlated to the

waves at frequencies COl and _ due to some nonlinear process, then a constant relative phase

relationship would exist and can be identified in the bispectral estimation, and, as a result, the asso-

ciated bicoherence will be equal to one. On the other hand, if the waves at frequency c01, r-02, and

COl+r.02, are totally independent of each other, then the phase of the bispectrum will remain random,

and the resulting bicoherence t stimation will be reduced to zero.

3. Envelooe Detection Analysis. The envelope detection method for bearing fault detection is

based on the observation that a bearing characteristic impact frequency (e.g., the rate of inner race

element passing) may modulate a bearing/machine structural or sensor resonant frequency. This

impacting will ring the particular resonant response at the characteristic repetition rate, or the modu-

lating frequency. Frequently, the harmonics of such beating impact excitations extend well into the

high-frequency region (10 to 100 kHz) to excite the structural or sensor resonant frequencies. The

resonant response of the monitored channel is then isolated and referred to as the carder frequency.However, what is of interest in the bearing analysis is not the carder frequency, but, rather, the

modulating frequency of the carder, or impact repetition rate. Demodulation of the resonant signals

(isolated through appropriate bandpass filtering) through use of a recovery algorithm such as theHilbert transform 9 method retrieves the instantaneous envelope signals. Subsequent spectral

analysis of the recovered envelope, i.e., postenvelope analysis, displays the complex frequency pat-tern related to the source bearing defect. Unlike the conventional vibration spectrum, the envelope

spectrum is void of rotor dynamic, hydrodynamic, and other sources of "noise" which tend to mask

the defect. This filtering of extraneous "noise" from pertinent distress signal content is accom-

plished through focusing analysis on variations in envelope intensity and not through the instan-

taneous signal intensity.

In this CDDF research, envelope analysis was applied to high-frequency data acquired from

both accelerometers and a single AE sensor. Envelope analysis using AE sensed input, although

more challenging in terms of digital acquisition and signal processing capability, offers several

potential advantages over the more traditional vibration sensing accelerometers. Since AE sensors

have extremely high-frequency response ranges (typically 200 kHz to 1.0 MHz), all conventional

low-frequency mechanical, hydrodynamic, and environmental responses along with electronic line

noise contributions are eliminated. Subsequent envelope analysis is thus allowed to focus on bearing

impact related energy which is captured at the AE sensor. Moreover, AE sensors are designed to

sense surface (Lamb) waves, which allows for better defect characterization at greater distances

from the impact source.

6

4. Postenvelope Bispectral Analysis. Through this CDDF-sponsored research, a unique

technological advancement opportunity relating to bearing defect analysis was made available, i.e.,the coupling of nonlinear bispectral signal analysis with AE-acquired bearing signatures recovered

from envelope analysis. The central innovativeness of this coupling of signal processing technologies

would be in its ability to effectively extract bearing fault patterns from the ultra-high-frequency

AE's. Success in this research would justify future efforts in applying the technology to more com-

plex machinery systems such as rocket engine high speed turbopumps. Hopefully, the postenvelope

application of nonlinear signal analysis to the AE-recovered bearing vibrations would increase

bearing fault detectability and allow identification of faults during their incipient stages.

In this CDDF research, bispectral analysis was successfully applied to AE-sensed bearing

signatures in a postenvelope mode, i.e., the nonlinear analysis was applied to recovered envelope

signals. Results were compared to those from conventional PSD analysis of low-frequency channels(<5 kHz), bispectral analysis of low-frequency channels, and finally with bispectral analysis of post-

envelope accelerometer acquired bearing signatures.

IV. ANALYSIS RESULTS

In this section, the analysis results from three of the bearing defect tests performed duringthis CDDF-sponsored research will be summarized. Several spectral and temporal data plots are

referenced extensively in describing research findings. Results from bearing defect signature recov-ery efforts for the following three bearing conditions will be discussed:

• Single-roller type bearing with no imbedded faults

• Single-roller type bearing with single axially extending scratch imbedded in inner raceway

• Single-roller type bearing with single axially extending scratch imbedded in one rollingelement.

A. Conventional PSD Analysis Results

In this research, the analysis bandwidth used in the conventional PSD analysis was 0 to

5 kHz since the frequency range effectively encompassed all expected characteristic bearing fre-quencies and modulation sideband families. Figure 3 is a compilation of 0- to 5-kHz PSD's com-

puted from the instantaneous signals acquired from two bearing block accelerometers for the three

bearing conditions of no imbedded flaw (figs. 3(a) and 3(d)), imbedded inner race defect (figs. 3(b)

and 3(e)), and imbedded rolling element defect (figs. 3(c) and 3(f)). As can be seen in the PSD's, all

spectra, including those from the clean bearing test, contain a multitude of frequency components

which makes identification of bearing defect signatures very challenging. Using best estimates of the

characteristic roller bearing frequencies (appendix), an attempt was made to identify analyticallypredicted bearing defect vibration patterns as developed in figure 2. For the roller beating inner race

defect case (figs. 3(b). and 3(e)), definite families of sidebands (sum and difference frequencies)

spaced by synchronous (rotor) speed, N, centered around harmonics of the predicted inner race

defect passing frequency (IRP) can be identified. In an effort to help display this structure in figure

3(b), a template has been superimposed on top of the PSD. Each IRP multiple is identified as aninteger with two synchronous-spaced intervals extending to either side. As can be seen in

figures 3(b) and 3(e), the first family of synchronous sidebands centered around the fundamental

7

inner racedefectpassingfrequency,IRP, is very muchindistinguishablein the 0- to 5-kHz PSD's ofthe two accelerometers.Also, theanalyticallypredictedlobing characterof the families of syn-chronousdependentsidebandsrelatedto thebeatingloadcycle is not obvious.For theroller bearingrolling elementdefectcase(figs. 3(c) and3(f)), neitherthe predictedfundamentalrolling elementspin frequency(RS), nor subsequentharmonicsarevisible in theconventionalvibration spectra.Infact, whencomparingroller defectcasePSD'sof figures3(c) and3(f) to thecleanbearingPSD's offigures 3(a) and3(d), the defectivebeatingspectracontainfar fewerdiscretecomponents,i.e., farfewer candidatesfor bearingfrequencies.However,subsequentpostenvetopeanalysisprovidesmore insight into the rolling elementdefecttest case.

Figure 4 showssampleinstantaneous acceleration waveforms over roughly 20 cycles of shaft

rotation (120-ms duration) which coincide with the PSD's of figure 3. The low-frequency (0- to

5-kHz) waveforms are dominated by rotor dynamic synchronous/synchronous harmonic reposes. No

evidence of impact type events related to the imbedded beating flaws can be seen in these low-

frequency characterizations.

B. Nonlinear/Bispectral Analysis Results, 0- to 5-kHz Data

In an attempt to better extract and confirm complex beating defect patterns in the conven-

tional low-frequency bearing spectra, nonlinear spectral analysis was applied. Specifically, bicoher-

ence spectra were generated for the defective beating test cases in an attempt to identify key har-monic and modulation sideband signal structures predicted analytically (fig. 2). Figure 5(b) shows

the bicoherence estimation for the inner race defect test case with a bispectral reference frequency

corresponding to the predicted roller inner race passing rate. In actuality, a tricoherence 6 7 estimation

with the first reference frequency set to zero, equivalent to a bicoherence estimation, was made. In

this case, bispectral analysis successfully identifies the complex modulation sideband families seen

in the corresponding PSD shown in figure 5(a). The bispectral analysis accomplishes this characteri-

zation of the vibration data by identifying key nonlinear phase couplings between individual members

of the families of bearing frequencies. Similar analysis was also applied to high-frequency data from

the rolling element defect test. Figure 6(b) shows the bicoherence estimation for the rolling elementdefect test case with the reference frequency set at the predicted roller spin frequency for the test

bearing. In the figure, no significant bicoherence peaks identifying nonlinear coupling between pre-

dicted bearing frequencies are apparent. In other words, unlike subsequent postenvelope analysis,

bispectral analysis of conventional vibration data was unsuccessful in diagnosing the bearing defect.

Apparently the roller defect vibration signature was much more subtle than originally anticipated.

C. Envelope Detection Analysis Results (Accelerometer Data)

Following the application of conventional PSD and nonlinear bispectral analysis to each

bearing test data set, envelope analysis of high-frequency accelerometer (10- to 80-kHz) and AE(50- to 400-kHz) data was performed. For data from both types of transducers, the Hilbert trans-

form method was used in recovering the envelope of the acquired original wideband instantaneous

waveforms. Once recovered, the envelope signals, which are themselves instantaneous waveforms

of the envelopes of the original time signals, were subsampled to provide analysis bandwidths (ofthe order) consistent with the conventional PSD and bispectral analyses previously discussed.

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Bearing block 90 ° location accelerometer spectrum for good roller bearing.

Bearing block 90" location accelerometer spectrum for roller bearing with inner race defect.

Bearing block 90 ° location accelerometer spectrum for roller bearing with rolling element defect.

Bearing block 270* location accelerometer spectrum for good roller bearing.

Bearing block 270 ° location accelerometer spectrum for roller bearing with inner race defect.

Beating block 270" location accelerometer spectrum for roller bearing with rolling elementdefect.

Figure 3. Conventional 5-kHz acceleration spectra across several test bearing configurations.

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Bearing block 90 ° location accelerometer waveform for good roller bearing.

Bearing block 90 ° location accelerometer waveform for roller bearing with inner race defect.

Bearing block 90 ° location accelerometer waveform for roller bearing with rolling elementdefect.

4(d). Bearing block 270 ° location accelerometer waveform for good roller bearing.

4(e). Bearing block 270 ° location accelerometer waveform for roller bearing with inner race defect.

4(f). Bearing block 270 ° location accelerometer waveform for roller bearing with rolling elementdefect.

Figure 4. Raw acceleration (0- to 5-kHz band limited) waveforms across several test bearing

configurations.

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Figure 6. Roller beating rolling element defect bicoherence estimation using bearing block 90*location accelerometer data (0 to 5 kHz) for a reference frequency set at rolling

element spin frequency.

Figure 7 is a compilation of PSD's (0 to 130 kHz), computed from the original time signals,used to characterize the wideband responses of the bearing block accelerometers recorded during the

testing. As can be seen in the PSD's, no prominent distinguishing features separate the responsesof the clean bearing test (figs. 7(a) and 7(d)), the inner race defect test (figs. 7(b) and 7(e)), and the

defective rolling element test (figs. 7(c) and 7(0). Bandpass filtering of the time signals with a

passband setting of 10 to 80 kHz was applied to all wideband bearing block accelerometer data priorto envelope analysis. This passband is denoted by the arrows in figure 7(a). Figure 8 shows the set

of envelope spectra, i.e., PSD's, recovered from the envelope of the wideband bearing blockaccelerometer signals. This sequence of PSD's (of the envelope signals) in figure 8 is consistent

with the conventional PSD sequence of figure 3 in terms of beating defect and accelerometer location.

12

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Bearing block 90 ° location accelerometer spectrum for roller bearing with inner race defect.

Bearing block 90* location accelerometer spectrum for roller bearing with rolling element defect.

Bearing block 270" location accelerometer spectrum for good roller bearing.

Bearing block 270" location accelerometer spectrum for roller bearing with inner race defect.

Bearing block 270" location accelerometer spectrum for roller bearing with rolling elementdefect. • ,

Figure 7. Wideband acceleration spectra across several test bearing configurations.

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Bearing block 90 ° location accelerometer envelope recovered spectrum for roller bearing withinner race defect.

Bearing block 90* location accelerometer envelope recovered spectrum for roller bearing withrolling element defect.

Bearing block 270* location accelerometer envelope recovered spectrum for good roller beating.

Bearing block 270* location accelerometer envelope recovered spectrum for roller bearing withinner race defect.

Bearing block 270* location accelerometer envelope recovered spectrum for roller bearing with

rolling element defect.

Figure 8. Envelope spectra derived from wideband acceleration signals across severaltest bearing configurations.

14

This allows direct comparison of the spectra of the envelope time signals to their corresponding

conventional low-frequency PSD's from the original time signals. As can be seen in the figures, the

complexity of the recovered envelope spectra is greatly reduced when compared to the ordinary

PSD's of figure 3. This is due in large part to the elimination of the lower-frequency synchronous and

broadband noise components. Figures 8(a) and 8(d), which are the recovered envelope spectra from

the clean (no implanted fault) bearing test, show spectral peaks at synchronous and a few higherharmonics of synchronous. These spectra are quite different from their corresponding low-frequency

PSD estimations seen in figures 3(a) and 3(d) that contain a multitude of discrete components.

Figures 8(b) and 8(e) are the spectra recovered from the envelope signals which contain the inner

race defect. The similarity of these recovered spectra to the analytically predicted pattern shown in

figure 2 is quite impressive. Every peak within the recovered envelope spectra can be linked to theinner race defect/synchronous sideband family structure. Also, note how subsequent harmonics of

the inner race roller-passing frequency (IRP), indicated as integers in the figure, exponentially decay

as predicted by the analytic model. 4 5 Moreover, both main lobes and side lobes in the synchronous

(N) sideband components of each IRP family are apparent and support the analytic predictions aswell. With the case of the rolling element defect, as seen in figures 8(c) and 8(f), the analysts were

pleased to detect clear evidence of roller spin frequency (RS) and its subsequent harmonics in the

envelope data. No substantial evidence of the spin frequency was found using both conventional PSDand nonlinear bispectral analysis of 0- to 5-kHz accelerometer data. Envelope analysis successfully

uncovered the subtle roller spin vibration predicted for the seeded defect.

As to why the clean-bearing envelope spectra contain discrete signal content at synchronous

and synchronous harmonics (figs. 8(a) and 8(d)), further analysis of similar no-defect roller bearings

would have to be performed to verify the recovery as an expected benign indicator.

Figure 9 contains the wideband instantaneous acceleration waveforms which correspond tothe envelope recovered spectra of figure 8. The time frame of the sequence coincides with the low-

frequency acceleration data shown in figure 4. Unlike the earlier figure, figure 9 depicts the transient

ringings initiated by the defective bearing surface impacts occurring during the defect tests. The best

examples of this are seen with the wideband acceleration data from the inner race defect test (figs.9(b) and 9(e)), where successive ringings of the structure are separated by one period of shaft

rotation, TN, or one bearing load cycle. The wideband responses of the roller defect case (figs. 9(c)

and 9(f)) are not quite as impressive, and, in fact, are quite similar to the clean (no fault) bearing

waveforms of figures 9(a) and 9(d). Regardless of this temporal comparison, spectral analysis of the

rolling element defect envelope waveform successfully recovers the bearing fault, distinguishing it

from a no-fault bearing.

D. Envelope Detection Analysis Results (Acoustic Emission Data)

In an effort to determine the effectiveness of AE monitoring, the same envelope analysis

applied to the wideband accelerometer data was also applied to AE data acquired during each bear-

ing test of the CDDF research. Although, digital signal processing requirements for the AE envelope

analysis effort were much more intensive given the envelope bandwidth, 50 to 400 kHz, for the AE's

was much higher and broader than that of the accelerometer input, 10 to 80 kHz.

Figure 10 shows the wideband AE spectra across the selected bearing test cases with the

envelope analysis bandwidth denoted in figure 10(a). A sensor output calibration error occurred inthe recording or reduction of the AE's from the inner race defect case, as can be seen by comparing

15

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Beating block 90 ° location accelerometer wideband waveform for good roller bearing.

Bearing block 90 ° location accelerometer wideband waveform for roller beating with inner racedefect.

Beating block 90 ° location accelerometer wideband wavefonn for roller bearing with rollingelement defect.

Bearing block 270* location accelerometer wideband waveform for good roller beating.

Beating block 270* location accelerometer wideband waveform for roller bearing with inner racedefect.

Beating block 270 ° location accelerometer wideband waveform for roller bearing with rollingelement defect.

Figure 9. Wideband acceleration waveforms across several test beating configurations.

16

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10(b). Wideband acoustic emission spectrum for roller bearing with inner race defect.

10(c). Wideband acoustic emission spectrum for roller bearing with rolling element defect.

Figure 10. Wideband acoustic emission spectra across several test bearing configurations.

the magnitude of figure 10(b) to those of figure 10(a) (clean beating) and figure 10(c) (roller defect),

but the error did not affect the overall results of the envelope recovery process. All three wideband

AE spectra are similar in that a majority of the spectral energy is contained in the bandpass region

selected for envelope analysis. Figure 11 shows the recovered envelope spectra of the AE's

recorded during the selected beating tests. As with the accelerometer-based envelope spectra, the

bearing signatures recovered in the AE envelope spectra are also quite impressive. Figure 1 l(a) is

the envelope spectrum recovered from the AE's recorded during testing of a good roller bearing. The

only distinguishable peaks in this spectrum are synchronous (N) related. Figure 1 l(b) is the AE

envelope spectrum from the inner race defect case. As with the accelerometer-sensed envelopespectra, it displays a bearing defect spectrum amazingly similar to analytically predicted model of

figure 2. As identified in the figure, families of discrete components with individual members of each

17

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11 (b). Acoustic emission envelope recovered spectrum for roller beating with inner race defect.

11 (c). Acoustic emission envelope recovered spectrum for roller bearing with rolling element defect.

Figure 11. Envelope spectra derived from acoustic emissions across several

test bearing configurations.

family separated by synchronous frequency are centered about the inner race roller passing frequency

(IRP) or its subsequent harmonics. As with the inner race bearing defect signature recovered from

the envelope of the wideband accelerometer data, the AE envelope spectrum for the inner race defect

case only contains spectral peaks related to the analytically predicted inner race roller passing/synchronous sideband family signal structure. The AE spectrum also exhibits the characteristic

exponential decay in the inner roller-passing frequency chain of harmonics. The spectrum also con-

tains the lobing patterns in the synchronous sideband components of each IRP family (fig. 11 (b)).

Figure 1 l(c) is the envelope spectrum recovered from AE's recorded during the rolling elementdefect test.

As with the successful accelerometer-based envelope recovery of the roller spin defect fre-

quency, AE envelope processing also uncovers the roller spin frequency and subsequent harmonics.

In addition, the AE envelope spectrum for the rolling element defect test (fig. 11 (c)) contains

18

evidenceof theroller train cagefrequency(C) alongwith its first harmonic(2C). The cagefrequencyis the analyticallypredictedmodulatingfrequencyfor therolling elementdefectcaseassynchronousfrequencyis for the inner racedefectcase.Lack of subsequentcage-separatedcomponentsaroundroller spin frequencyandspin harmonicsin figure 1l(c) is mostlikely a result from insufficient sideloadsbeingapplied to the rotor assemblyduring testing.

Figure 12containsthe widebandinstantaneousAE waveformsthat correspondto therecoveredenvelopespectraof figure 1I. Thesefiguresrelay thehighly transient"ringings" initiatedby the defectivebearingsurfaceimpacts,assensedby the AE sensorduring the bearingdefecttests.Again, the inner racedefectcase(fig. 12(b))showsthis transientnaturebest.In the figure, aseriesof successiveringings, separatedby an interval of approximatelyoneperiod of shaft rotation,Try,is very evident. Again, aswith the widebandaccelerometercharacterization,the roller defectcase,figure 12(c)is not as impressive,and,in fact, is quite similar to the cleanbearingAE waveformof figure 12(a).Regardless,spectralanalysisof theroller defectAE envelope(fig. 1l(c)) recoversthe correctbearingdistressindicators (RSandRS harmonics)for theseededfault.

Figure 13effectively highlights severalkey observationsmadeduring the researcheffort. Itcomparestheenveloperecoveredspectra,from both the accelerometersandAE sensor,to the con-ventionallow-frequency(0- to 5-kHz) accelerationspectrumfor the inner racedefectcasealongwith the analytically predictedbearingdefectvibrationspectrum.Peakidentification hasbeenpur-poselyomitted in the figure to allow for betteroverall patternperception.Figure 13(a)is thecon-ventional0- to 5-kHz vibration spectrumassensedby a bearingblock accelerometerduring theinner racedefect test.The ability of envelopedetectionto "'filter out" nonbearingdefect-relatedinformation is very evidentby comparingthebasicPSDof figure 13(a)to theenvelopespectraoffigures 13(b) and 13(c).While figure 13(a)containsbearingdefect-relatedfrequencycomponentsalongwith rotor dynamic/structuralresponsesandelectronicline noise,all frequencieswithin theinner roller defectenvelopespectraarebearing-defectdriven andcanbedescribedby the inner rollerpass/synchronoussidebandmodulationpatternpredictedin figure 13(d).The intent of this summary-type figure is twofold. First, it delineatesthe successof envelopeanalysis(figs. 13(b) and 13(c)) bycomparingits resultsdirectly to the analyticallypredictedvibrationspectrumfor the single-pointbearingdefect (fig. 13(d)).This correlationwassuccessfullydemonstratedusing bearing distresssignal contentcontainedin radically different frequencybands,i.e., theaccelerometerenvelopesig-nalswererecoveredfrom the 10- to 80-kHzband,while the AE envelopesignalwas recoveredfromthe 50- to 400-kHz band.This dual successin bearingdefectsignaturerecoverythroughenvelopeanalysisusing two totally different sensortechnologies,accelerometerand AE, is encouraginginthat it identifies multiple analysispathsto thesamediagnosticsolution.Again, what is of interest inenvelopedetection-basedbearinganalysisis not the carrier frequency,which dictatestype of sen-sor,but, rather, the modulatingfrequencyof thecarrieror bearingimpactrepetition rate. Secondly,andmore importantly, figure 13highlights theeffectivenessof envelopedetectionin isolating bearingdefect information from extraneoussystemnoisesuchasrotor dynamicandenvironmentalfeed-throughs.

Figure 14comparesthecorrespondinginstantaneouswaveformsof the low-frequency (0- to5-kHz) accelerometersignal (fig. 14(a)),the wideband(10- to 80-kHz) accelerometersignal(fig. 14(b)), and the wideband(50- to 400-kHz) AE's (fig. 14(c)) for the inner racedefect test case.While the transientimpacteventsresultingfrom testingwith the imbeddedbearingflaw areeffec-tively maskedin the low-frequencycharacterizationof figure 13(a),both the widebandaccelerometerand AE responseseffectively capturethem.Figure 14(d)is the instantaneousenvelopewaveformcorrespondingto theAE's of figure 14(c).Direct PSDanalysisof this envelopewaveform resultedinthespectrumof figure 13(c).

19

00450

-080000.0400

ae clean

v

a

e 12 (a) .

e 12 (b) .

12(c).

t 2(a). Acoustic emissions from good roller beating.!

• 12(b). Acoustic emissions from roller bearing with inner race defect.

12(c). Acoustic emissions from roller bearing with rolling element defect.

Figure 12. Acoustic emission waveforms across several test beating configurations.

20

bbgO ra',v

bb90 env

aeenv

[ • i " ]

,,,,,I_';_'__¸I_Lt,_,','!,'ii

0 _E+flOr --_ [E

F-O1F 61' ............ ] ,

0 1000 2000

0 I[£ _ 03r

3000 4000 5000

T

F

0 1000 2000 3000 4000 50:_n'3 lE /_t_ ........... 7 7 ............ '

G IOCO _000 3000 4000 f_OI)(_

13 (a) .

13(b).

13 (c) .

/J

/I

!

s :II'0

Envelope due Io exponential decay

_ _ _ _# Main lobes

,2f, // "'_/ / 11 t _, / I

_ / ' ,,! _1 " " '

l t,:,,ul -'],,./ , ,, ,,n I ["'_,1_ r/ ,

X_,._It,,/',.,I_TSide-lobe_

_- f

13 (d)

13(a). Bearing block 90 ° location 0- to 5-kHz acceleration spectrum for roller bearing with inner racedefect.

13(b). Bearing block 90 ° location 0- to 5-kHz envelope recovered spectrum for roller bearing withinner race defect.

13(c). Acoustic emission envelope recovered spectrum for roller bearing with inner race defect.

13(d). Analytically predicted bearing defect vibration spectrum (McFadden and Smith4).

Figure 13. Comparison of envelope recovered spectra to conventional vibration PSD and analytically

predicted vibration spectrum, roller bearing inner race defect case.

21

I0.0000

bb90.raw

-6.0000

700.000_'

bb90-wband

ae-wband

ae.env

-450.0000

0011111 0.3500 °

0.0090

0.00

14 (b)

k__

2

14 (c)

]__rl I i ('][}_ , , , nil4 i , , N_]_ , 1 o p8 , i (]_ , r , l ;2

0.02 0.04 0.06 0.08 0 ] 0 O, 12

14(a). Bearing block 90* accelerometer low-frequency (0- to 5-kHz) response for roller beating withinner race defect.

14(b). Bearing block 90* accelerometer wideband (0- to 130-kHz) waveform for roller bearing withinner race defect.

14(c). Acoustic emission response (50 to 400 kHz) for roller bearing with inner race defect.

14(d). Envelope of waveform shown in figure 14(c).

Figure 14. Comparison of wideband and envelope beating defect waveforms to conventional(0- to 5-kHz) acceleration waveform for inner race defect case.

E. Postenvelope Bispectral Analysis Results

The final leg of this CDDF-sponsored research effort was devoted to the application of non-linear spectral analysis to the envelope signals of acquired wideband instantaneous acceleration and

AE data, i.e., postenvelope bispectral analysis. Given the success of bispectral analysis with con-ventional low-frequency bearing data (0 to 5 kHz), signal content from the bandwidth where actual

characteristic defect frequencies reside, application of nonlinear analysis performed on the envelope

of the defect signals, in order to investigate the additional benefits gained, seemed a logical exten-

sion of the two technologies.

Figure 15 shows both the bicoherence (fig. 15(b)) and regular PSD (fig. 15(a)) spectra for an

accelerometer-sensed wideband envelope time signal from the inner race defect test. Resulting highbicoherence estimates (fig. 15(b)), using a reference frequency set at the inner race roller pass

22

88237

E9 151

13 833 _

I 2.276 !_

10 60,1 it _

9.327

8 240

7810

6610

57 6646

o10000

0995

0992

0990

0988

0.983

0 982

0982

0.981

0.980

X=bb,9Opcb 63.00r •

{ 15(a) .

15(b).

15(a). 0- to 5-kHz PSD of bearing block 90 ° accelerometer wideband response envelope.


set at inner race roller passing impact rate.

Figure 15. Roller bearing inner race defect bicoherence estimation using envelope of bearing block90 ° accelerometer data, reference frequency set at inner race roller passing impact rate.

impact rate, not only confirm the harmonic relationship between the inner race passing frequency and

its multiples, but also the synchronous sideband (sum and difference) components around the pass-

ing frequencies. Figure 16 shows the corresponding bicoherence (fig. 16(b)) and PSD (fig. 16(a))

envelope spectra recovered from the AE transducer recorded during the inner race defect test.

Excellent results were obtained with the AE data in that not only the harmonic and sideband signal

structure related to the defect was verified, but that superior signal-to-noise ratio in the bicoherence

estimate was evident relative to the accelerometer-based estimate shown in figure 15(b). Figures

17 and 18 show similar bicoherence estimations for both accelerometer and AE envelope data,

respectively, for the rolling element defect case. As with the inner race defect case, bicoherence

analysis using the bearing impact frequency as a reference confirms the harmonic relationship

between the fundamental impact frequency and subsequent harmonics.

23

O. IE-03

168.6 0.000020

9795 o.3E-05

I148o 03E-05

1313.7 0.2E-05

2293.2 O. IE-05

2]27.5 O. I E-05

34412 0.9E-06

2,161.8 0.9E-06

3609.8 0.7E-06'

COkIP= 0 0349

PSD

O. IE-O_

1.0000

168 6 0.914

2293.2 0.693

2461.6 0.882

1313.7 0.874

979.5 0.870

2124.6 0.868

1958.9 0.866

3441.2 0.836

810.9 0823

(Amp-Phase)NAVG= 116

l]W= 2906

000000

ae-test3.hi/ X=bb.180ae S+ 63.00

I I I I [ I I I

I I _ I [ i I i

I

1000

'I'RI-COIIi;:I_ENCE Txxxx(f I =

t I J l t i I i I _ , i I i i

2000

Freq

O.O0,f2=

3000

f3 (hz)

1 147.3o,r',{)

4000

4_J

I

I

J

16(a)..

16 (b).

J

:1I

16(a). 0- to 5-kHz PSD of acoustic emission wideband response envelope.


set at inner race roller passing impact rate.

Figure 16. Roller bearing inner race defect bicoherence estimation using envelope of acousticemissions, reference frequency set at inner race roller passing impact rate.

24

O, IE+02

354 3 1.700

167 2 1310

708 6 0901

35 8 0.809

5(35 6 0.529

1058 9 038"

14132 025!

17675 010

COMP= 22 2056

PSD O. IE_OI L_. _

10000 , , ,-i

1058 9 0677

354 3 0857

708 6 O 647

14132 0568

17675 0472

4180 0408

772.3 0 383

8957 0383

63 7 0.382 I

(Amp Phase)

NAVG I71

f];_' 3 08 I

0 O00Oo

ae.testT/ENV X=bb-9Opcb S+ 40.00

TRI- C0tlF;RENCE Txxx,_lrl =

2000 3QOO

Freq r3 (hz)O.O0,f2= 353.70,f3)

17 (a} .

17 {b).

17(a). 0- to 5-kHz PSD of bearing block 90 ° accelerometer wideband response envelope.



Figure 17. Roller bearing rolling element defect bicoherence estimation using envelope of bearingblock 90 ° accelerometer data, reference frequency set at rolling element spin frequency.

25

O.IE-07

354.7 0.6E-08

64,0 0,5E-08

706.4 0.3E-06

127,9 0,3E-08

337.2 0.3E-08

I061.0 0.2E-08

1412.8 0.7E-09

] 767.4 0.5E-09

2122.1 0.4E-09

COMP= 0.0014

PSD

ae.testT.hi/

' IX=bb-18Oae S+ 40.00

T

18(a).

O. IE-09

1.0000

706.4 0,719

351.7 0,664

[061,0 0657

[412.8 0.534

1348.8 0.488

I01.7 0.485

287.8 0.461

2119.2 0.457

2773.3 0.441

(Amp-Phase)

NAVG= 91

BW= 2.906

18(b).

0.00000 I000 2000 3000

Freq f3 (hz)

TRI-COHERENCE Txxxx(fl = O.00,f2= 353.70,f3)

4000 5000

18(a). 0- to 5-kHz PSD of acoustic emission wideband response envelope.



Figure 18. Roller bearing rolling element defect bicoherence estimation using envelope of acoustic

emissions, reference frequency set at rolling element spin frequency.

Since the envelope spectra for these single-point bearing defect cases under controlled lab-

oratory conditions have easily recognizable harmonic/modulation sideband patterns in them, applying

nonlinear signal analysis to enveloped bearing data may not appear to have added diagnostic value.

However, these successful applications prove the concept of coupling the two technologies, thereby

laying a foundation for future applications. Future applications will undoubtedly require nonlinearanalysis to sort out unidentifiable complex harmonic/modulation sideband patterns resulting from

multiple-point defects both within and across heterogeneous beating sets under complicated opera-tional conditions.

V. SSME TURBOPUMP BEARING EXAMPLE

During developmental testing of the full scale SSME alternate design high pressure oxygen

turbopump (ATD HPOTP), one preliminary design of the high speed turbopump consistently experi-

enced extensive pump-end ball bearing wear. The beating wear developed during hot-fire testing

26

was typically accompaniedby a transientthermalshift in the bearingcoolantcircuit asregisteredbythe temperaturedifferential acrossthebearingcoolantflow. During thebearingfault investigationefforts, dynamicanalysisfocusedon extractingevidenceof pump-endball bearingdistressin high-frequencydatachannelsacquiredduringSSMEhot firings. Conventionallow-frequency,0 to 5 kHz,pumphousingaccelerationspectradid provideevidenceof ball spin (BS) frequenciesor its side-bandsseparatedby the ball train cagefrequency(C). During the investigation,bispectral analysisofthe low-frequencyaccelerometerdatasuccessfullyverified severalsubtle bearingfrequenciesin thedynamicdata.

In anattemptto bettercharacterizethebeatingcondition during the degradationincidents,envelopeanalysisof pump-endaccelerometerwidebandhigh-frequencydatawasperformed.Figure 19showsseveraldynamic indicatorsjust prior to anATD HPOTPpump-endball bearingincident that occurred during SSME hot fire 904-159 in December of 1992. Figures 19(a) and 19(b)

are 0- to 25-kHz and 0- to 5-kHz pump-end accelerometer PSD's, respectively, reflecting pump

housing dynamic response 90 s before a beating incident. Figure 19(c) is the envelope spectrumgenerated from the envelope signal recovered from the wideband response, 15-kHz to 25-kHz

frequency band, of the pump-end accelerometer. Figure 19(d) is a representative portion of the

instantaneous time signal of this wideband wavefonn used to calculate the envelope spectrum of

figure 19(c). During this time frame, there is no evidence of bearing-related frequencies in either theconventional or wideband envelope spectra. The discrete components in the conventional PSD's of

figures 19(a) and 19(b) are primarily synchronous (N) or synchronous harmonics due to rotor

dynamic and inducer/impeller blade wake responses.

Figure 20 shows the same series of dynamic indicators 90 s later into the hot fire when a

significant thermal shift occurred across the pump-end ball bearing coolant circuit. First, notice the

increased discrete activity in the 15-kHz to 25-kHz frequency band shown in figure 20(a) as

compared to the same PSD taken 100 s earlier, start plus 20 s, shown in figure 19(a). Also, notice

the corresponding new subtle peaks in the 0- to 5-kHz band shown in figure 20(b). However, the

best dynamic indication of the bearing distress occurring during the thermal shift time frame is shown

in the recovered envelope spectrum of figure 20(c). Where the corresponding envelope PSD

estimated 90 s prior to the event (fig. 19(c)) shows absolutely no evidence of discrete bearing wear

indicators, the envelope spectrum estimated during the event (fig. 20(c)) displays a distinct ball spin

(BS) and cage (C) sideband modulation pattern similar to those recovered in this CDDF-sponsoredresearch. Notice the similarity of the recovered SSME ATD HPOTP envelope spectrum to the

analytically predicted defect spectrum predicted by McFadden and Smith 4 5 shown in figure 2. The

recovered envelope spectrum exhibits not only the characteristic exponential decay in the ball

passing frequency (BS) chain of harmonics, but it also exhibits the lobing sideband characterattributed to bearing load cycle. Figure 20(d) shows the corresponding wideband acceleration time

signal, over approximately 20 shaft revolutions, from which the envelope spectrum was developed.

Notice the increase in the peak-to-peak intensity of the waveform as compared to the same

indicator taken prior to the bearing event (fig. 19(d)). Bispectral analysis was also applied to the

envelope data in an attempt to confirm the modulation sideband pattern related to the beatingdamage. Although some degree of nonlinear coupling between spectral peaks in the envelope PSD

(fig. 21(a)) was detected, the amplitudes of the bicoherence estimates of the envelope signal using areference frequency of twice the ball spin (BS) rate (fig. 21(b)) were nowhere near the levelsdetected in the CDDF test cases (figs. 15(b), 16(b), 17(b), 18(b)). These lower levels of

bicoherence for this ATD HPOTP example can most likely be attributed to bearing configuration.

Traditionally, nonlinear bicoherence analysis applied to ball-type rolling element bearing defect data

yields lesser coherences than those developed from roller-type bearings. However, as shown in

figure 21 (b), bispectral analysis of the envelope signal is still useful in that it recovers some degree

27

of nonlinearcorrelationbetweenthebearingcomponentscontainedin the correspondingenvelopespectrum.Finally, figure 22 showsa time-frequencymappingof the pump-endaccelerometerenvelopesignalover the completebearingdegradationincidenthot-fire test.Overlaid on thismappingis the ATD HPOTPpump-endball beatingcoolantflow delta-temperature(dischargeminusinlet). As can be seenin the figure, thereis anunmistakablecorrelationbetweenthe delta-temperatureexcursionand recoveryof the discretebearingdistresscomponentsin the envelopeofthewidebandaccelerationtime signal.

psd s+20s

psd s+20s

env. psd

O.IE+03_ ' ' ' ' I .... I .... I .... I ....

4t,, ,e_,,

19 (a)

O.IE-03

O'IE+03 f ._NNN 10000 15000 20000 _f 00

8_

r19(b)

100(1 :9000 _000 4000 5 }0

wband time

O.IE-03

O.IE+O0

0.1E-02

35.0000

-35.0000

} 10_NI I I I I I I I i

i i i i I i i I i

20.01

:_NflO t

20.02

_NN' ' I

_1 I I I I20.03

40(]0 5f' ' ' ' i ' ' ' I

, , , t 1 , , , , I -20,04 20.05

19 (c)

O0

19 (d)

19(a). Pump-end accelerometer 0- to 25-kHz conventional PSD.

19(b). Pump-end accelerometer 0- to 5-kHz conventional PSD.

19(c). Pump-end accelerometer 0- to 5-kHz envelope PSD.

19(d). High-frequency (15- to 25-kHz) waveform from pump-end accelerometer during PSDestimation times of figures 19(a), 19(b), and 19(c).

Figure 19. High-frequency characterization of SSME alternate HPOTP vibration immediately priorto pump-end ball bearing degradation incident.

28

psd s+120s

psd s+120s

O. Ig+03_ ' ' ' ' I ' ' ' r I ' ' ' _ I ' ' ' ' i _ _ ' ' =

0. IE-03_ j j j , I _ , , t J i , I K ] i i t 1 l 1 _ , I =F,n(3(_ _(3f_fl(3 I _CIn(3 ?CIC_fWl

O. IE+03'- = ' ' ' ' I ' ' _ J ' _ ' ' I , _ , , ] , l , ,

r . Z_ A g"

: 7f] ;t[ z_s zc z_ _-zd

O.IE-O3 i J _ i I i l i J I I i _ _ I i J i _ 1 I i i i :

I IflnO 2Clf)O :"I_ f')CI ,_._ .'%_"O00.1E+O0 : ,' , ' ' _ I J _ ' r I ' ' [ _ ' ' _ _ I ' ' _ ' -

! ' ii,,.... I['LI.....III'iI' !°

, , J _ } i , _ r I _ J , , I , i , , .I J , , ,1 nrwl 2 flrln .q_lln a Nr_(_ .51300

, ' ' .' ' I. ' ' '..' I ' I ' ' ' I, ' ' ' ' I ' ' ' ' I'A

I ZO.O 1 I _0.0_ I_0.03 I _0.04 120.05

env. psd

O.IE-02

35.0000

wband Lime

35.0000

20(a) .

O0

20{b).

20(c) .

20{d),

20(a). Pump-end accelerometer 0- to 25-kHz conventional PSD.

20(b). Pump-end accelerometer 0- to 5-kHz conventional PSD.

20(c). Pump-end accelerometer 0- to 5-kHz envelope PSD.

20(d). High-frequency (15- to 25-kHz) waveform from pump-end accelerometer during PSD

estimation times of figures 20(a), 20(b), and 20(c).

Figure 20. High-frequency characterization of SSME alternate HPOTP vibration during pump-endball bearing degradation incident.

29

O.IE+O1

152.63 0.10|

1709.40 0,074

76.31 0.056

1556.76 0.034

1858.97 0.034

305.25 0.032

228,94 0.032

1098.90 0.021

2164.22 O.Ol[

COMP= 5.9836

PSD

O.IE-02

1.0000

15263 0.403

228.94 0.347

2316.65 0.343

3052,50 0.313

2991.45 0.307

2710.62 0.292

2164.22 0.276

1056.17 0.271

1971.92 0.261

(Amp-Phase)

NAVG= 98

BW= 3,052

0.0000

904.159A/ENV X=PBP RAD 2193 S+ 120.00- ' ' ' ' I ' ' ' ' l ' ' ' ' I ' ' ' ' E ' ' _ '

l , , , Ii I i I ' ' ' ' I ' ' ' ' "I '

.o.

I I I i I I I I

I l I I I I I I

1000 4000 5000

TRI-COHERENCE Txxxx(fl =

21(a)

21(b)

2000 3000

Freq f3 (hz)

0 O0,f2= 1709.40,(3)

21 (a). 0- to 5-kHz PSD of pump-end accelerometer wideband response envelope.

2 l(b). 0- to 5-kHz bicoherence spectrum corresponding to PSD of 21(a), reference frequency set atpump-end ball beating ball spin frequency.

Figure 21. ATD HPOTP bicoherence estimation using envelope of wideband pump-end

accelerometer data, reference frequency set at pump-end ball bearing ball spin frequency.

30

npk = 90

lenmed= 70

neigh = 0

ipara = 10

IT1X = 5

my = 2

nPSDs = 468

nAVGs = 5

BW = 3.1

HANN/ 3

TIME - sec

02/03/95

I;11)23

0

0 I000 _000 3000 4000

FREQ (Hz)

Figure 22. Joint time/frequency mapping of SSME alternate HPOTP pump-end accelerometer

recovered envelope signal during pump-end ball bearing degradation incident hot-firetest with ball bearing coolant circuit delta-temperature overlay.

VI. CONCLUSIONS/RECOMMENDATIONS

In summary, this CDDF-sponsored research successfully applied nonlinear signal analysis in

the identification of the complex signal structures associated with several single-point bearing defect

vibrations. The application was successful using both conventional and AE-type sensor technology.Moreover, bearing defect signatures were identified using both low-frequency, within the band

containing the characteristic bearing frequencies themselves, and wideband dynamic data where

impacts from seeded bearing defects "rang" structural and sensor resonances. Recovered envelope

bearing spectra are strikingly similar to those analytically predicted in literature in that they display

symmetric lobing character in sideband structures related to the bearing load cycle. They also

consistently show a characteristic exponential decay in the fundamental impact rate harmonic series

which is also predicted analytically. This CDDF-sponsored research demonstrated the successful

31

applicationof nonlinearbispectralanalysison recoveredenvelopetime signalsin identifying complexbearing signatures. Such coupling of technology will undoubtedly be required in future diagnostic

evaluations of complex rotating machinery in advanced propulsion systems. Finally, the benefit of

AE monitoring for bearing defect analysis was confirmed in the research. In this research, both

wideband accelerometer- and AE-sensed responses (signals from two very different frequency

regimes) successfully identified seeded test bearing defects. However, in real-world applications,

accelerometer-based envelope detection analysis may be unable to recover bearing distress

information in complex rotating machinery systems since it is vulnerable to structural noise. In light

of this, AE-based envelope detection offers an attractive alternate diagnostic solution.

This accomplished research can be extended in several directions. An in-depth study into

bearing defect detection sensitivity thresholds using the diagnostic tools identified in this research,

particularly the expanded use of AE technology, would be of great benefit. Research could extendinto the characterization of more complex forms of beating signatures including those from multiple-

point defects along with subtle wear patterns such as rolling element-to-element nonuniformities.

Also, further application of the diagnostic tools to commercial applications in the transportation,

power, and manufacturing industries would undoubtedly prove the techniques' utility.

32

REFERENCES

.

2.

.

.

,

,

,

,

9.

Harris, T.A.: "Rolling Bearing Analysis." John Wiley and Sons, 1966, pp. 203-207.

Tandon, N., and Nakra, B.C.: "Defect Detection in Rolling Element Bearings by AcousticEmission Method." Journal of Acoustic Emission, vol. 9, No. 1.

Holt, J., Palmer, I.G., Glover, A.G., Williams, J.A., and Darlaston, B.J.L.: "Some Examples of

Laboratory Application and As_ssment of Acoustic Emission in the United Kingdom."

Proceedings of the International Institute of Welding Colloquium on Acoustic Emission, 1975,

pp. 35-37.

McFadden, P.D., and Smith, J.D.: "Model for the Vibration Produced by a Single Point Defect in

a Rolling Element Bearing." Journal of Sound and Vibration, vol. 96, 1984, pp. 69-82.

McFadden, P.D., and Smith, J.D.: "The Vibration Produced by Multiple Point Defects in a

Rolling Element Bearing." Journal of Sound and Vibration, vol. 98, 1985, pp. 263-273.

Jong, J., Jones, J., Jones, P., Nesman, T., Zoladz, T., and Coffin, T.: "Nonlinear Correlation

Analysis for Rocket Engine Turbomachinery Vibration Diagnostics." 48th Meeting of theMechanical Failure Prevention Group (MFPG), April 1994.

Jong, J., Jones, J., McBride, J., and Coffin, T.: "Some Recent Development in TurbomachineryFault Detection." NASA 1992 Conference on Advanced Earth-to-Orbit Propulsion

Technology, May 1992.

Bendat, J.S.: Nonlinear System Analysis and Identification." John Wiley and Sons, 1990.

Dugundi, J.: "Envelopes and Pre-Envelopes of Real Waveforms." I.R.E. Transactions onInformation Theory, IT-4, No. 1, March 1958, page 53.

33

APPENDIX

ROLLER BEARING FREQUENCY CALCULATIONS

JEFFCOTT BEARING FREQUENCY CALCULATION-MathCAD file JEFFCOTT BEARING FREQ

Modification for Jeffcott rotor kit SKF roller bearing NU 202 ECP

Dimensions in milli-meters (mm)

d:=5.5 D dm := 23.8125 Nb := ii

rad _ 1

8i := _i-deg

:= pi - Ho

d

deg - rad

180

HO := _o-deg

BT :=

1 - --'cos(Hi)

dm

1 + cos(A)

OBP := Nb-BT

IBP := Nb- (i - BT)

BS := ...... cos (8i)

2 d

8i= 0

d-5.5

Contact angles is zero for

roller bearings

Ho = 0

Cage Outer Ball Pass Inner Ball Pass

BT = 0.385 OBP = 4.23 IBP = 6.77

Ball Spin

BS = 2.049

_7 _._/q,_._,o._86

I_REt_iI_IG _ _;ImlTl" _ 37

d 15-20 mm

0.5906-0.7874 in

t 'I

r3_ [ID D1 ..... d F

j jType NU

r2

dl ....

j_Type NJ

Principaldimensions

I

--.- cl dl

Type NUP

r4

r

Type N

Basic load ratingsdynamic static

d O B C Co Pu

mm

in

Fatigue Speed ratingsload Lubncationlimit grease oil

Mau Designation

NIbf

N r/rainIbf

38

150.5906

170.6693

2O0.7874

35 11 12 500 10 200 1 220 18 0OO 22 000 0.0471.3780 0.4331 2 810 2 290 274 0.10

35 11 12 500 10 200 1 220 18 000 22 000 0,0491,3780 0,4331 2 810 2 290 274 0,11

42 13 19 400 15 300 1 860 16 000 19 000 0,0861.6535 0,5118 4 360 3 440 418 0,19

42 13 19 400 15 300 1 860 16 000 19 000 0.0881.6535 0,5118 4 360 3 440 416 0.19

40 12 17 200 14 300 1 730 16 000 19 000 0.0681.5748 0,4724 3 870 3 220 389 0,15

40 12 17 200 14 300 1 730 16 000 19 000 0.0701.5748 0,4724 3 870 3 220 389 0,15

40 12 17 200 14 300 1 730 16 000 19 000 0.0731.5748 0,4724 3 870 3 220 389 0.16

40 12 17 200 14 300 1 730 16 000 19 000 0.0661.5748 0,4724 3 870 3 220 389 0.15

40 16 23 800 21 600 2 650 16 000 19 000 0,0921.5748 0.6299 5 350 4 860 596 0.20,

40 16 23 800 21 600 2 650 18 000 lg 000 0.0951.5748 0.6299 5 350 4 860 596 0.21

40 16 23 800 21 600 2 650 16 000 lg 000 0.0971.5748 0.6299 5 350 4 860 596 0.21

47 14 24 600 20 400 2 550 14 000 17 000 0.121.8504 0.5512 5 530 4 590 573 0.26

47 14 24 600 20 400 2 550 14 000 17 000 0.121.8504 0,5512 5 530 4 590 573 0.26

47 14 24 600 20 400 2 550 14 000 17 000" 0.131.8504 0,5512 5 530 4 590 573 0.29

47 14 24 600 20 400 2 550 t4 000 17 000 0.121,8504 0.5512 5 530 4 590 573 0.26

47 14 25 100 22 000 2 750 13 000 16 000 0.111.8504 0.5512 5 640 4 950 618 0,24

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47 18 29 700 27 500 3 450 13 000 16 000 0,141.8504 0,7087 6 680 6 180 776 0.31

47 18 29 700 27 500 3 450 13 000 18 000 0.141,8504 0.7087 6 680 6 180 776 0.31

52 15 30 800 26 000 3 250 12 000 15 000 0.152.0472 0,5906 6 920 5 850 73t 0.33

52 15 30 800 26 000 3 250 12 000 15 000 0.152,0472 0,5906 6 920 5 850 731 0.33

52 15 30 800 26 000 3 250 12 000 15 000 0.162,0472 0,5906 6 920 5 850 731 0,35

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52 21 41 300 38 000 4 800 11 000 14 000 0,212.0472 0.8268 9 290 8 540 1 080 0.46

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APPROVAL

BEARING DEFECT SIGNATURE ANALYSIS USING ADVANCED NONLINEAR

SIGNAL ANALYSIS IN A CONTROLLED ENVIRONMENT

CDDF FINAL REPORT (NO. 93-10)

By T. Zoladz, E. Earhart, and T. Fiorucci

The information in this report has been reviewed for technical content. Review of any informa-

tion concerning Department of Defense or nuclear energy activities or programs has been made by

the MSFC Security Classification Officer. This report, in its entirety, has been determined to beunclassified.

J.C. BLAII_

Director, Structures and Dynamics Laboratory

_U. S. GOVERNMENT PRINTING OFFICE 1995 6331--109 / 20043

39

Form Approved

REPORT DOCUMENTATION PAGE o_8 _o. o7o_-o188

Public re)offing burden for this collection of information is estimated ¢o average 1 hour i;>er reSaOnse, including the time for reviewing instructions, searching exlstincJ data sources,

gathering a.n d maintaining the d4ta needed, and compdet!n<j and reviewing the collection of *nformatiOn. Send comments regarding this burden estimate or any other as_t of thiscollectiOn of information, including suggestions for reducing th*s burden, to Weshmgton Headauarxer_ Set.aces. Oarec_orate for Information OPerations and Rel:)orts. t215 Jefferson

ORris Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget. Paperwork Reduction Pro ect (07040188). Washington, OC 20503.

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE I 3. REPORT TYPE AND DATES COVERED

Ha.7 1995 I Technical Memorandum4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Bearing Defect Signature Analysis Using Advanced NonlinearSignal Analysis in a Controlled EnvironmentCDDF Final Ret)ort (No. 9_-lQ)

6. AUTHOR(S)

T. Zoladz, E. Earhart, and T. Fiorucci

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION

George C. Marshall Space Flight Center REPORT NUMBER

Marshall Space Flight Center, Alabama 35812

9. SPONSORING / MONITORING AGENCY NAMEIS) AND ADDRESS(ES) : 10. SPONSORING / MONITORING

National Aeronautics and Space Administration AGENCYREPORTNUMBER

Washington, 13(2 20546-0001NASA TM-108491

11. SUPPLEMENTARY NOTES

Prepared by Structures and Dynamics Laboratory, Science and Engineering Directorate.

12a. DISTRIBUTION/AVAILABlUTYSTATEMENT

Unclassified-Unlimited

12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200words)

Utilizing high-frequency data from a highly instrumented rotor assembly, seeded bearingdefect signatures are characterized using both conventional linear approaches, such as power spectraldensity analysis, and recently developed nonlinear techniques such as bicoherence analysis.

Traditional low-frequency (less than 20 kHz) analysis and high-frequency envelope analysis of bothaccelerometer and acoustic emission data are used to recover characteristic bearing distress

information buried deeply in acquired data. The successful coupling of newly developed nonlinearsignal analysis with recovered wideband envelope data from accelerometers and acoustic emissionsensors is the innovative focus of this research.

14. SUBJECTTERMS

bearing diagnostics, nonlinear signal analysis, acoustic emission,

turbomachinery health monitoring

17. SECURITYCLASSIFICATION18. SECURITYCLASSIFICATION19.OFREPORT OFTHISPAGE

Unclassified Unclassified

NSN 7540-01-280-5500

SECURITY CLASSIFICATIONOF ABSTRACT

Unclassified

15. NUMBER OF PAGES

48

16. PRICE CODE

NTIS

20. LIMITATION OF ABSTRACT

Unlimited

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Documents

Bearing Defect Signature Analysis Using Advanced Nonlinear