AN INNOVATIVE CABLE FAILURE DETECTION AND PHM TOOLSET.pdf

8/10/2019 AN INNOVATIVE CABLE FAILURE DETECTION AND PHM TOOLSET.pdf

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AN INNOVATIVE CABLE FAILURE DETECTION AND PHM TOOLSET

James Hofmeister and Sonia Vohnout

Ridgetop Group, Inc.3580 West Ina RoadTucson, AZ 85741

Telephone: (520) [email protected]

[email protected]

Abstract: Cabling degradation represents a large segment of complex system-maintenance budgets. In addition to submarines, unmanned underwater vehicles (UUVs)use submersible cables. Surface ships, aircraft, ground vehicles and industrial machines

that encounter vibration or adverse environmental conditions can also benefit fromRidgetops cable failure detection and PHM toolset. This paper presents a non-invasive,ruggedized toolset consisting of a personal hand-held device that uses an innovative,frequency sweep-based analysis approach to detect damage to multi-conductorsubmersible high data rate (SubHDR) cables. The toolset also includes an innovativeadaptive time-to-failure (ATTF) prognostic algorithm to generate accurate remaininguseful life (RUL) estimates for detected damaged cables.

An electronic hand-held device to detect degradation, such as a cable kink and/or brokenstrands in a multi-strand conductor in SubHDR cables, greatly reduces maintenance costsand improves mission reliability. SubHDR cables, such as those used in the sensor mastson the Virginia class of submarines, are integral to the sensor masts, and physicalinspection requires removal and disassembly of the large, heavy masts themselves. Theaddition of a prognostic algorithm to provide fast, accurate time-to-failure (TTF) orremaining useful life (RUL) estimates supports deferred maintenance paradigms: asopposed to the costly and time-consuming immediate removal and replacement of a mastthat, although the SubHDR cable is damaged, its damage might not be significant enoughto cause cable failure before completion of the next mission. The new electronic detectionmethod can readily be adapted for commercial use, including multi-strand cables that arenot submersible or integrated in antennas.

Key words: Diagnostics; prognostics; health management; submersible high data

rate cable; HDR; SubHDR; remaining useful life; time-to-failure

Introduction: SubHDR cables, such as those used in the sensor masts on the Virginiaclass of submarines (as shown inFigure 1), are physically integrated in the sensor masts.Current electronic checking methods are limited to only continuity and resistancemeasurements, and unless the wire is completely broken, continuity can still be verified.The current methods do not detect whether any strands in a wire are broken or whether a
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coaxial conductor is fractured. Broken strands in a wire could lead to cable failure in amatter of days or weeks, depending on the severity of the wire fatigue.

Figure 1: Virginia class submarine with sensor mast attached to the sail

Periodic inspection of outboard multi-conductor cables with a means to identify potential

failures allows the U.S. Navy to predict the service life and replace cables before acomplete open-circuit failure occurs during at-sea operations. Physical inspection is notan acceptable detection method because doing so requires the removal and disassemblyof the large, heavy sensor masts themselves. A hoisted cartridge system of sensor mast isshown inFigure 2).

Figure 2: Hoisting a universal modular mast (UMM) cartridge

What the Navy requires is a personal hand-held, ruggedized device capable ofpinpointing potential failures in outboard cables that are subjected to harsh environmentsday in and day out, including submersion, extreme temperatures, high hydrostatic

pressure, bending, or any other external force that decreases useful life. Furthermore, theinspection method(s) must be non-invasive. The Navy wants the solution set to include anaccompanying prognostic algorithm to accurately predict the remaining service life ofdamaged, but not yet failed, outboard cables; remaining service life is also calledremaining useful life (RUL).

A personal hand-held device will provide the methods and means to support periodicinspection of outboard multi-conductor cables with the capability to identify and locate


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potential failures without having to remove and disassemble the sensor mast. Theproposed prognostic algorithm will provide the Navy with an RUL capability (accurateestimation of the remaining service life). Cables can be replaced before a complete open-circuit failure (severed cable) occurs during at-sea operations.

The innovation described in this paper will meet the Navysneed: a non-invasive methodto detect non-open-circuit damage and an RUL calculation engine coupled with anadvanced prognostic algorithm to meet a Navy desire for a service life predictioncapability, and provide a damage location capability. These methods, housed in a hand-held tool, will provide a significant improvement in maintenance methods, and reduce theNavys support costs.

Background: Submersible high-data rate (SubHDR) cables, such as those used in thesensor masts on the Virginia class of submarines (as shown inFigure 1 andFigure 3), arephysically integrated in the sensor masts. A lingering malfunction is that they are subjectto kinking and eventual open-conductor failures. The cable is drawn through a roller

mechanism as the mast is raised and lowered, and a kink occurs when a cable twist formsdue to torsion and tension action, and then tightens under subsequent tension increase.

Figure 3: Mast to cable-roller relationship

One antenna-mast cable consists of three twisted, stranded conductors within a grounded,braided mesh: 19 copper strands comprise each conductor.Figure 4 shows a section ofthe three conductors with the grounded mesh removed; the circled area shows an exampleof the type of kink that occurs[1].The damage progresses until the wire strands withinthe conductor begin to fracture (see Figure 5), eventually resulting in a complete open.The cables are failing much sooner than the expected life of five years.


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Figure 4: Twisted triplet 24-AWG segment showing a kink (grounded shield is removed)

Figure 5: Microscopic camera view of a fracture failure

Current electronic checking methods are limited to continuity and resistancemeasurements, which may still be verifiable unless the wire is completely broken.Current assessment methods do not detect whether any strands in a wire are broken or acoaxial conductor is fractured. Periodic inspection of outboard multi-conductor cableswith a means to identify potential failures allows the Navy to predict the service life andreplace cables before a complete open-circuit failure occurs during at-sea operations.Physical inspection is not an acceptable detection method because doing so requires theremoval and disassembly of the large, heavy sensor masts.

This paper will discuss Ridgetopstwo innovative approaches, which are discussed in thefollowing sections.

Sample Mode Response Technique (SMRT) Sweeping Probe and SensorProcessing Unit (SPU): SMRT is a method of applying a stimulus signal to a device or

unit for a short period of time (the sample), capturing the response to the stimulus, andusing digital signal processing techniques to characterize the response, then extractingeigenvalues that comprise a fault-to-failure progression (FFP) signature (see Figure 6).The SMRT sweeping probe and SPU is a replacement solution to a time domainreflectometry (TDR) method, which determines whether the cable is damaged andprovides information about some of the damage locations.


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Figure 6: SMRT Sweeping Probe and SPU

This innovative approach1:1. Provides the same functionality as TDR.2. Overcomes a deficiency in the TDR approach. The TDR approach requires a great

deal of transmission energy to identify all of the fault locations that might bepresent in, for example, a 50-foot cable.

3. Is a proven technology:o

it uses a well-known frequency sweep in the GHz rangeo it uses well-known signal amplification and analog-to-digital data conversiono it uses well-known (digital) fast Fourier transforms (FFT) and the well-known

inverse fast Fourier transforms (IFFT).4. Uses the easy-to-characterize E-ratio as an FFP signature. This method is easy

compared to using either the TDR reflection amplitudes or time locations.5. Overcomes the inability of TDR to locate a large number of damage locations. In

the tests we performed, using commercial TDR equipment, we were able toidentify only three of the eight damage locations in an 11.5-foot cable sampleprovided by the Navy.

Adaptive Remaining Useful Life Estimator (ARULE) Program and theAdvanced Time-to-Failure (ATTF) Algorithm

2: ARULE [3] is a program

consisting of an application-specific front-end module that interfaces to an ATTF kernelmodule (seeFigure 7)to adapt a fault-to-failure progression (FFP) signature model to thedata, and uses the adapted model to produce estimates of the RUL of the device,

1Patent applied for.2Patent pending final approval.

MastKink

SMRT

Sweeping

Probe

Cable: Digital SignalsAmplified RF reflections

Sensor Processing Unit

--------------------

Hull

Connection

Digitize Received RF

Save Digitized data

Perform FFT (DFT)

Save FFT output

Perform IFFT

Save IFFT output

Perform DSP Analysis

Find Reflections

Calculate E = ER/EICalculate SoH

Operator Input

Electronic Input

SubHDR Cable Type

TEST, RESET

SAVE, CLEAR

TRANSMIT

DOWNLOAD

--------------------

Cable Specifications

Last Adapted Model(s)

Saved Files

[time E] to ARULE

Bulk Data Download to PC

E value

SoH %

PASS/FAIL

SPU Display


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component, or assembly to which the FFP refers. The ARULE program and the ATTFalgorithm support the creation of a model using E-ratio data, and they verify that themodel is correct.

Figure 7: ARULE and the ATTF kernel

This method:1. Provides a real-time, condition-based remaining service life capability to the

Navy.2. Is a proven technology. ARULE, with an earlier version of a prognostic

algorithm, has been delivered to the U.S. Army for use with power supplies. Theenhanced prognostic algorithm (ATTF) has been fully tested and benchmarked.

Experiment and Results: The design and architecture of the solution determines the

state of health (SOH), the number and locations of damage, and estimated RUL ofSubHDR antenna-mast cables. This solution set is a SMRT Sweeping Probe connected tosensor processing unit (SPU), which sends FFP signature data to an ARULE programwith an ATTF RUL estimating algorithm. The SMRT Sweeping Probe, under the controlof the SPU, generates a sweeping radio frequency (RF) of up to 2 GHz into the antenna-mast cable (see Figure 8). Damage in the form of kinks, fraying, crushing, and brokenconductor strands introduces a change in impedance at the damage locations, andimpedance change causes RF waves to be reflected back to the source.

The RF reflections are captured, amplified, and sent by the probe to the SPU. The SPUthen digitizes and saves the amplified response from the probe and uses digital signal

processing functions, such as FFT (fast Fourier transform) and IFFT (inverse FFT), andother DSP processing to find the locations and amplitudes of reflected energy. The SPUthen calculates an energy ratio, E, of the maximal reflected energy to the transmittedenergy.

ARULE Program

Front-end

Program

Module

ATTF Kernel

Program

Module

Dimensionless

Data

RUL

Estimate

Data

File

Saved

Condition-based

Data

Save

&

Retrieve Data

ARULEApplication

Display

From SPU

SoH: 75%

RUL: 29


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Figure 8: SMRT Sweeping Probe block diagram

Experiments were conducted on cables provided to Ridgetop by the Navy. A damagedcable had eight visible kinks.Figure 9 shows the plots of inverse fast Fourier transformsfor an undamaged cable (with unmatched input and output impedances) on the left, andfor a damaged cable on the right.

Figure 9: Plots of the IFF transformundamaged cable (left) and damaged cable (right)

The results and analysis of the experiment on an undamaged cable exhibit three peaks,each caused by the characteristic inductive impedance of the cable, the load, and the

reflected signal. An undamaged cable is expected to produce three such peaks. Theundamaged cable was a twisted three-wire conductor with 18 AWG stranded wires.

The results and analysis of the experiment on the damaged cable show that there is theincident peak of the input (blue circle) and nine reflection peaks (red circles): one for theend of the cable and eight for each kink in the cable.

The SPU calculated maximal value of E and the time of the sweeping sample are sent toARULE to calculate an estimated RUL. For quick diagnostic purposes, the SPU will senddigitized IFFT data to a small display along with human-readable values of the E-ratio, astate-of-health assessment, and a pass, damaged, or failed condition. The SPU saves data

for both processing purposes and for downloading to support post-processing analyses.

Prognostic Results:As a first step in preparing ARULE for making RUL estimations forSubHDR cables, we plotted the candidate FFP signatures. We then selected one of thethree candidate signatures and created a prototype model. We then created a test data setfor ARULE modeling. We used the prototype model in ARULE to produce RULestimates and verified the modeling by processing the data collected for cut cable strandsat the 1.2 foot and 6.4 foot locations.


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Figure 10 shows the plot of each of the three candidate FFP signatures, and the plotsreveal all three signatures have a very similar characteristic shape, which is good becauseit simplifies our choice of which to use as the FFP signature.

Figure 10: Plot of the three candidate FFP signatures

There were three candidate measurements for use as FFP signatures: Maximum reflected

energy (Rmax), the ratio of reflected energy to incident energy (I-ratio), and the ratio oftransmitted energy to reflected energy (E-ratio). We selected E-ratio because it is anormalized ratio unlike Rmax, which uses absolute dB measurements or the I-ratio,which is not normalized with respect to amplitude. The E-ratio is a normalized,dimensionless value with 1.0 as the maximum value. A test data set was then created (seeFigure 11)for ARULE modeling, using averaging and extrapolation, so we could analyzethe accuracy of the model and because the two data sets from our investigativeexperiments were somewhat sparse. As seen, the test data set (squares) retains thecharacteristic shape of the FFP signature and the amplitude and time [event] information.

Figure 11: Plot of experiment (circles and diamonds) and test (squares) data


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After creating the model definition in our ARULE front-end program, we ran the test.Figure 12 shows the input data set, the floor threshold (bottom horizontal line), theceiling threshold (top horizontal line), the input model definition (blue line), and theadapted model (pink line). As seen, and as expected for a fitted model, there is only aslight adjustment between the initial model and the adapted model.

Figure 12: Test data set showing floor, ceiling, and models

Figure 13 shows the plots of the RUL estimates and the differential nonlinearity (DNL)for those estimates. The results are a positive DNL of 1.9%, a negative DNL of -5.3%,and an overall DNL of 7.2%. The small red circle on the horizontal line of the RUL plot

is the time-at-failurepoint.

Figure 13: RUL and DNL plots for the SubHDR test data


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The last step was to verify the original data can be accurately processed by ARULE usingthe model we created. Figure 14 shows the data collected from cutting strands of thecable conductor at the 1.2-foot location.

Figure 14: E-ratio data for the 1.2 cut location

This is a very sparse data set, yet the RUL estimates are very accurate with a total DNLof only 2.0% as seen inFigure 15.

Figure 15: RUL estimates and DNL for data from the 1.2 cut location

Figure 16 shows the E-ratio data collected from cutting strands of the cable conductor atthe 6.4-foot location. This is also a very sparse data set.


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Figure 16: E-ratio data for the 6.4 cut location

As seen inFigure 17,the RUL estimates are also accurate: the total DNL is only 3.5%.

Figure 17: RUL estimates and DNL for data from the 6.4 cut location

Conclusion and future developments: The authors identified and verified the feasibilityof developing two non-invasive significant and innovative approaches to providing theNavy with hand-held device(s) capable of pinpointing potential failures in outboardcables that are subjected to harsh environments including submersion, extremetemperatures, high hydrostatic pressure, bending, or any other day in and day out externalforce that decreases useful life. In addition we presented a prognostic algorithm to

accurately predict the remaining service life of damaged, but not yet failed, outboardcables. The solutions and the prognostic algorithm options are the following: (1) SMRTSweeping Probe connected to sensor processing unit, and (2) ARULE program using anATTF algorithm.

As we move forward toward the goal of providing a hand-held device for navalpersonnel, some of the near-term future work includes the design and simulation of aprototype probe device and processing unit that differs from the vector network analyzer


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(VNA) equipment used in our s-parameter experiments described in this paper. Thesignificant differences are the following:

1. Reduced complexity2. Reduced disk support3. Reduced graphical display support

4.

Reduced weight, power, and cost. The previously described reduced functionalityand support translates into reduced weight, reduced power consumption, andreduced cost3to design, build and test.

5. Increase in ease of use. By reducing the complexity and display options, and byusing built-in tables and calculations, our SMRT Sweeping Probe and SPU willoffer naval maintenance personnel equipment that does not require RF andnetwork expertise in order to operate the unit and understand the results.

6. Cables and connectors will be designed for below-the-hull SubHDR connectivityinstead of very expensive BNC coaxial connections.

References:

[1]

Bryan Hoffman, NAVSEA technical point of contact for this SBIR program,provided us with pictures and an 11.5-foot sample of a kinked, 3-conductor cable.

[2]B.P. Lathi, Modern Digital and Analog Communication Systems; HRW Series inElectrical and Computer Engineering. 1983

[3]Time Domain Reflectometry Theory; Application Note 1304-2, Agilent Technologies2006

[4]James Hofmeister and Sonia Vohnout; Adaptive Remaining Useful Life Estimator;ISHM 2009

[5]Kojovic, L.A.; Williams, C.W., Jr.; Sub-cycle detection of incipient cable splicefaults to prevent cable damage;Power Engineering Society Summer Meeting, 2000.IEEE Volume 2, 16-20 July 2000

[6]

Cooper, E.S.; Dissado, L.A.; Fothergill, J.C.; Application of thermoelectric agingmodels to polymeric insulation in cable geometry; Dielectrics and ElectricalInsulation, IEEE Transactions on [see also Electrical Insulation, IEEE Transactionson] Volume 12, Issue 1, Feb. 2005

Acknowledgement:

We greatly appreciate the assistance and support given to us by our Navy technical pointsof contact during initial Small Business Innovation Research (SBIR) contract # N65538-10-C-0039 to design and develop this technology.

Biography:

[1] James Hofmeister is a senior principal research engineer and engineeringmanager. He has been a software developer, designer and architect for IBM andrepresented IBM as a member of the board of directors of the Southern Arizona Center

3The full-function Agilent VNA equipment we used in our s-parameter experiments has a base price ofapproximately $125,000 with options ranging to $5,000 and more. And that equipment has no prognostic orstate-of-health capability.


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for Software Excellence. At Ridgetop Group, he is a principal investigator and leaddesign engineer, specializing in analog and digital circuit designs for electronicprognostics. He is a co-author on six U.S. patents (three IBM and three Ridgetop), twoother pending Ridgetop patents, and four filed invention disclosures. He retired fromIBM in 1998 after a 30-year career and joined Ridgetop Group in 2003. He earned a BS

in electrical engineering from the University of Hawaii, Manoa Campus, and an MS inelectrical and computer engineering from the University of Arizona.[2] Sonia Vohnout earned her MS in Systems Engineering from the University ofArizona in Tucson. With a diverse background and experience, Sonia is well-suited tomanage Ridgetops commercialization efforts from its many government-funded projects.Sonia joined Ridgetop after successfully building an electronic subassembly business inMexico, working as a Systems Engineer at IBM, and handling overseas installations ofsoftware with Modular Mining Systems (now part of Komatsu). During her career, shehas held executive management and senior technical positions. In addition, Sonia has co-founded several companies. Sonia is a board member of the Society for MachineryFailure Prevention Technology (MFPT) (www.mfpt.org), an interdisciplinary technical

organization strongly oriented toward practical applications. Sonia recently founded thePrognostic and Health Management (PHM) Professionals LinkedIn Group(www.linkedin.com), a fast-growing group whose objectives are to discuss PHM-relatedtopics, network with others in the PHM community, and increase awareness of PHM.

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AN INNOVATIVE CABLE FAILURE DETECTION AND PHM TOOLSET.pdf