Rotary Wing Dynamic Component Structural Life Tracking ... AHS2010.… · American Helicopter Society International, Inc. be reduced and robustness increased 1. This paper builds

Session: HUMS/CBM

Presented at the American Helicopter Society 66th

Annual Forum, Phoenix, Arizona, May 11-13, 2010.

Copyright 2010 by the American Helicopter Society International, Inc.

All rights reserved.

American Helicopter Society International, Inc.

Rotary Wing Dynamic Component Structural Life Tracking with Self-Powered Wireless Sensors

KCF Technologies, Inc. 112 W. Foster Ave.

State College, PA 16801

ABSTRACT

KCF Technologies has developed a novel wireless

load sensing technology that can be readily embedded

in existing rotorcraft parts located in critical load paths

for use in HUMS systems. An H-60 pitch link rod end

manufactured by Lord Corporation was chosen as the

development platform. The rod end has an existing

cavity within the threaded stem where components are

embedded. These components consist of a

piezoelectric stack for energy harvesting, a

magnetostrictive based load sensor, data acquisition

hardware, RF transmitter, and all accompanying circuitry. Experimental testing at Lord Corporation

demonstrated the system’s ability to harvest energy at

representative pitch link loading levels, continuously

sample load sensor data at 100 Hz, and wirelessly

transmit the data to a nearby location. Flash memory

can also be embedded within the rod end to store

critical parameters derived from pitch link loading

history. When not in service, the part could therefore

be interrogated to determine its individual fatigue

damage accumulation and remaining service life

leading to improved aircraft readiness and reduced

maintenance costs. KCF is working directly with partners Lord Corporation, Sikorsky Aircraft, and

Goodrich to develop the core technology subject to

critical performance and design constraints. KCF is

also collaborating with Technical Data Analysis, Inc.,

to address the essential objectives of tracking each

aircraft and its components in near real-time with a

system level approach.

INTRODUCTION

Basing rotorcraft component replacement on

individual component damage accumulation supports

the DoD objectives of improved aircraft readiness and

reduced maintenance costs. This concept requires

accurate and thorough sensing, automated diagnostics,

prognosis models to assess component condition and

system integrity, and a data management infrastructure

for directing appropriate maintenance activities. In

spite of the clear benefits of achieving this transition to

component damaged-based replacement, it is difficult

due to the large scope of the CBM infrastructure needed and the low tolerance for error in its

performance.

Although implementation of the data management

system requires a substantial effort to carry out, it has

a high likelihood of practically succeeding as a viable

commercial and military system. Similarly, the

diagnostics and prognostic models can be developed

with substantial work but low technical risk. Accurate

collection and supply of data to the data processing

and management system poses the greatest technical

risk since it must be a part of the rotorcraft structure

and most approaches will have many potential failure points. In addition, the rotorcraft sensor system weight

and robustness must ultimately be compared to simply

increasing component size/strength and factors of

safety.

The focus of this paper is the high technical risk

task of accurately monitoring component damage

accumulation. Zakrajsek, et al, indicate that there is a

strong case for implementing load monitoring on

helicopter rotor assemblies, if sensor system cost can

Jacob Loverich Jeremy Frank Joseph Szefi Edward C. Smith

Director of Research, KCF

Technologies, Inc.

President, KCF

Technologies, Inc.

President, Invercon, LLC Professor, Penn State

University

Member, AHS Member, AHS Member, AHS Member, AHS

Jacob Loverich Jeremy Frank Joseph Szefi Edward C. Smith

Director of Research, KCF

Technologies, Inc.

President, KCF

Technologies, Inc.

President, Invercon, LLC Professor, Penn State

University

Member, AHS Member, AHS Member, AHS Member, AHS

Zach Fuhrer

Product Engineer

LORD Corporation

Enrique Flores

Rotary Wing Str. Eng.

NAVAIR Struc 4.3.3.2


be reduced and robustness increased1. This paper

builds on the substantial amount of work that has

already been done in this area. Since the scope of a

rotorcraft wide health monitoring system is

significantly larger than the project that is the basis for

this reporting, a strategic approach was taken to narrow the effort of monitoring the load transferred

through uniaxial load members that have rod ends.

This work supports the concept that data from a few

optimally placed load sensors will enable derivation of

a wide range of rotor component loads by using

advanced structural models like those being developed

by TDA (Technical Data Analysis, Inc.).

Elastomeric rod ends are ideal platforms for load

monitoring because they are located on critical load

links on main rotor dampers and pitch link assemblies,

as pictured in Figure 1. KCF Technologies, Inc. and

LORD Corporation have developed a rod end load monitoring system that improves upon work in the

area of wireless load monitoring on aircraft by

addressing issues related to system level robustness

and component level integration. The rod end system

is intended to supplement existing HUMS systems by

providing real time load data on the rotor assembly.

Since the rod end is a common aircraft component, the

sensor system is applicable to other locations

including landing gear.

Figure 1 Potential application of KCF’s embedded

load monitoring technology on rotorcraft

KCF is integrating energy harvesting, load sensing, and wireless communication for enabling the

rod end’s autonomous and real time load monitoring.

The new load sensor addresses the need for low

power, EMI robustness, and calibration free sensing.

The energy harvester enables battery-free operation of

the sensor system with 0.3 inch package size. The

wireless RF communication uses recent advances in

low-power wireless radio communication. The

complete system is self-contained inside the threaded

stem of the rod end, rendering a durable and

maintenance–free sensor.

OBJECTIVES

The overall goal of this work is to develop

fundamental tools for wireless load sensing

technology for rotorcraft that can locally harvest

energy using embedded components. The primary objectives pursued to reach this goal are the following:

1) Identify appropriate energy harvesting and load sensing methodologies.

2) Develop custom, ultralow power data acquisition and wireless transmission

components.

1) Experimentally validate the performance of

the embedded components in an H-60 pitch

link rod end undergoing representative

loading conditions.

BACKGROUND

Rotorcraft Health Monitoring

Energy harvesters and wireless sensors enable

complete elimination of liabilities relating to

hardwiring of health monitoring sensors. Because

batteries require periodic replacement and are sensitive

to extreme temperature environments, energy harvesters are an ideal solution for many applications.

The combination of wireless data transmission and

energy harvesting provides a cost effective and robust

approach to implementing sensor systems.

In such systems, sensors measure various health

indicators such as strain, temperature, pressure, and

vibration (acceleration). Each sensor node in this case

is autonomous and maintenance free in that the energy

required to acquire and transmit sensor measurements

will be harvested, rather than supplied by a wired

power source.

One such existing approach to wirelessly monitor rotor loads has been developed by Microstrain, Inc2,3.

This approach measures rotor loads with a

piezoresistive sensor applied to the exterior of the

pitch link. Exterior piezoelectric patches provide

energy harvesting for load sensing and wireless

transmission power requirements. The system was

flight tested on a Bell 412 helicopter and was capable

of sampling and wirelessly transmitting data at a rate

of 64 Hz in flight. Because the load monitoring and

energy harvesting components are located on the pitch

link’s exterior, however, this system may be readily subject to damaging impacts during helicopter


operation or routine maintenance. A more robust

solution would be to package these components into

existing parts in critical load paths. KCF has

accomplished this by embedding both harvesting and

load sensing components within a pitch link rod end

stem. To provide a clear path to commercialization, KCF is collaborating directly with the rod end

manufacturer, Lord Corporation, to incorporate the

components during the manufacturing process.

Embedded Component Load Monitoring

Measuring the structural response of mechanical

systems subject to loading provides a fundamental

means for characterizing the local response of

components or the global inputs to systems or

components. For example, strain gauges on helicopter

pitch links are used to measure loads being transmitted through the pitch links to the rotor assembly.

Alternatively, strain gauges on rotor blades could be

used to determine the actual stress state of the blade

for estimating its damage accumulation during use.

Use of bondable metal foil strain gauges have

been by far the most prominent strain and load

measurement technique for the past half century. Foil

strain gauges have a network of thin wires embedded

in a thin plastic film. When subject to strain, the metal

foil stretches, which in turn changes the length to

cross-sectional area ratio of the metal wire. The geometric changes of the wire alter the electrical

resistance of the gauge. Because the changes in

resistance are small, Wheatstone bridge circuits are

used to amplify the electrical response.

Their small size, minimal influence on the host

structure’s mechanical properties, and ease with which

they can be added to a structure have made them

useful for many applications particularly in temporary

installations or in diagnostic implementations on

existing structures. Significant drawbacks of these

sensors, which typically do not limit their application

in light of other traditional strain measurement technology, include temperature sensitivity, change in

performance over time due, and the power electronics

needed to operate them. The temperature sensitivity is

minimized using low thermal expansion metals alloys

such as nickel-copper.

For some applications, the mentioned drawbacks

make their use particularly unattractive. For example,

strain measurement accuracy on helicopters is critical

due to the small safety factors. Changes in the

sensitivity over time are unacceptable, especially

because these strain gauges are often used for updating component retirement times to reduce operating cost,

and a strain gauge that requires frequent calibration

would add cost and maintenance. With advances in

ultra low power wireless sensors, the power

requirements and electronic circuit required for foil

gauges are further limiting their applicability for some

applications.

Of the alternative and innovative strain sensing

technologies, including optical fiber Bragg, piezoresistive, and capacitive, the inverse

magnetostrictive sensors are the most promising for

discrete low power sensor nodes. Fiber Bragg sensors

are particularly well suited for applications where

large power electronics are acceptable, cost is not a

concern, and many measurement locations along a

single fiber are needed. Piezoresistive sensors suffer

from strong temperature sensitivity, and capacitive

sensors typically require complicated power

electronics and signal processing.

The strain sensor developed by KCF utilizes the

inverse magnetostrictive effect. This effect characterizes the change of domain magnetization

when a stress is applied to a material, which is also

known as the Villari effect. On a micro scale,

application of a magnetic field causes boundaries

between the domains to shift and the material's

dimensions to change. A Hall effect sensor is used to

measure changes in the magnetic permeability of the

magnetostrictive material. The sensor voltage output

can then be calibrated to actual load levels. The

advantages of this approach include very low power

requirements and the ability to put the sensor in sleep mode when data is not acquired.

In the current work, KCF has seamlessly embedded

this sensor in a pitch link rod end’s interior so that

information recorded about the rod end’s health remains

with the rod end. In this case, removing the rod end from

its assembly will not disassociate the health data from the

rod end. This is important for air and land vehicle

components that are periodically removed and may

remain in storage for a period of time. Since retrieval of

health data from the sensor depends on the operation of

the energy harvesting power supply, the power harvester

must be subject to its intended environment. When a component with an embedded sensor and power

harvester is in storage or removed from its assembly, the

energy harvester is likely to be completely inert and it

would not be possible to extract health data from the

component. This is an issue because one of the main

purposes of embedded health sensors is to evaluate if a

component can be returned to service. KCF is currently

developing technologies that will allow for local

interrogation of uninstalled components that rely on

embedded energy harvesters for power. Current designs

consist of connectionless interrogation wands that can quickly provide wireless power and communicate with

the component to assess its health when not in use.


Energy Harvesting

To harvest energy from a host object’s mechanical

strain, piezoelectric transducers are directly attached

or embedded in the host component, where the

piezoelectric material strains with the host component. This strain on the piezoelectric element in turn induces

charge at its electrodes. The charge is then extracted

by the circuit so that it can be delivered to the health

monitoring device.

This strain-based harvester is implemented by

placing the piezoelectric material in the load path.

Since the electric field generated in the piezoelectric

material is proportional to the strain, the voltage at the

electrodes is minimized by using thin layers of

material. In this configuration, the d33 piezoelectric

constant defines the relationship between strain and

electric field. To increase the power output of the harvester while still minimizing voltage, the

piezoelectric material is layered to form a piezoelectric

stack. The piezoelectric element is coupled

mechanically to the host structure by placing it under a

preload using the host structure’s surrounding

structure. In this way, the element is in parallel with

the primary stiffness of the host structure.

Energy is harvested from the electromechanical

transducer using a circuit that consists of a diode

bridge, a DC-DC converter, energy storage element,

and voltage regulator. Input voltage protection and overvoltage protection for the storage element are also

included in the circuit. The bridge provides

rectification of the voltage waveform from the energy

harvester transducer. The uni-polar rectified

waveform is fed into an input capacitor that acts as a

voltage filter so that a steady DC voltage is available

for the DC-DC converter. The DC-DC converter

optimally transfers energy from the input capacitor(s)

which are at variable voltages to a much larger sized

energy storage element that is typically used within a

narrow voltage range4. The energy storage element

provides an energy reservoir so that the load can draw power that is much higher than that supplied by the

harvester for short durations. This is needed because

the electrical load is generally determined by its

specific application and it is often much higher than

that which is available directly from the energy

transducer.

Infrastructure for Dynamic Component Life

Tracking

For effective component tracking and life assessment, it is imperative that up-to-date history and

remaining life of components be readily available

through a sophisticated system architecture that

smoothly integrates all pertinent data management

systems. KCF is collaborating with technology partner

TDA to address the essential objectives of tracking

each aircraft and its components in near real-time with

a system level approach, gathering complete

component usage history and thereby accurately predicting the life of each component, and

subsequently eliminating penalties imposed due to

unknown usage histories. KCF’s sensor development

supplements this system with accurate and near real-

time load data on the rotor assembly.

TDA envisions one comprehensive and integrated

dynamic component tracking system. This vision

brings together rotorcraft data in one open architecture

framework to provide near real-time component health

and fatigue life expended (FLE) values. The fleet

management tool envisioned in this framework will

promote the development of safety strategies through asset management via prognostics, scheduling fleet

maintenance actions, and future acquisitions.

TECHNOLOGY DEVELOPMENT

Load Monitoring H-60 Rod End with Embedded

Components

An elastomeric H-60 pitch link rod end was

chosen as the development platform for KCF’s

rotorcraft load monitoring technology. The rod end,

pictured in Figure 2, was provided by the Lord

Corporation. As indicated in the figure, the energy

harvester, load sensor, data acquisition circuit board,

harvester circuit board, and RF communication circuit

board are all embedded within an existing cavity that is approximately three quarters of an inch in diameter

and four inches deep. The loads applied to the rod end

in flight cause dynamic straining of the part, which in

turn cause straining of the piezoelectric harvesting

element. The dynamic voltage created in the

piezoelectric element is collected by the harvester

circuit. The harvested power is then delivered to the

load sensor and the data acquisition and RF

communication boards. The magnitude of the

dynamic flight loads is sufficient to power all of the

embedded electrical components and RF communication devices.


Figure 2 Load monitoring H-60 rod end with

embedded KCF components

Magnetostrictive Load Sensor

The magnetostrictive load sensor currently in

development at KCF consists primarily of a magnetostrictive material, a Hall effect sensor, and

permanent magnets located at either end of the

magnetostricive material. A change in stress to the

load sensor results in a change in the magnetic

permeability of the magnetostricive material. This

change in permeability alters the amount of magnetic

flux that is passing perpendicularly through the Hall

effect sensor. This change in magnetic flux

corresponds to a change in the Hall voltage output,

which can then be calibrated to the load. The Hall

effect sensor is ultra low power and compact, with a

geometry of 2.0 x 3.0 x 0.75 mm, and is capable of being put into sleep mode when not being

interrogated.

Figure 3 Magnetostrictive load sensor and

piezoelectric energy harvesting stack

Energy Harvesting

The embedded energy harvesting element consists

of a piezoelectric stack element that generates a

voltage in proportion to the level of strain it

undergoes. The piezoelectric stack within the rod end

is capable of providing in excess of 5 mW of power

while the rod end is subject to representative pitch link

loading.

A comparison of the harvester power generation

and the power used by the sensor for various operation

modes is plotted in Figure 4. The power generation

depends directly on the magnitude of load applied to the rod end, and the power consumed is approximately

proportional to the data sampling rate.

Data Acquisition and Wireless Transmission

An ultra low power microprocessor is employed

to sample the load sensor data. The microprocessor

has a 12 bit A/D for high resolution conversions, a

DMA engine for efficient and power sensitive

transferring of data, multiple timers for

synchronization, and large amounts of on-board RAM

for larger transfers of data packets. The data acquisition code is interrupt driven to take advantage

of the different low power modes of the processor.

The RF link has over the air data rates of up

to 2Mbits/sec, which halves the duty cycle over

traditional 1Mbits/sec RF radio. This in turn reduces

the number of over the air data packet collisions that

could occur. The radio also has the capability of auto-

acknowledging receptions of data packets, up to three

times, without any microprocessor involvement. Flash

memory components ranging from 4 – 128 Mb can

also be added to store sensor history and rainflow counting.

Standard IEEE 802.15.4 protocols such as

Bluetooth® and Zigbee® are designed for specific

applications that are different enough from rotorcraft

low power sensor applications that a proprietary RF

communication is required. The wireless solution

presented herein addresses the need for a lower

payload overhead and high data transmission rate

while maintaining a high level of robustness. In

addition, KCF’s wireless communication system will

comply with the US government’s FIPS 140 security

standard. Antenna design is a critical feature for highly

reliable data transmission and in terms of optimal

mechanical packaging. A few antenna designs were

evaluated for application to the rod end sensor. Patch

style antennas provided a balance between size and

attenuation. Chip antennas for direct implementation

on a PCB and helical whip antennas were also

characterized. A summary of preliminary antenna

testing is shown in Figure 6.

RF Antenna

Harvester

Load Sensor

Harvester Circuit, Microprocessor, and RF

Transmitter

1.5”

0.7”

Energy Harvester

Load Sensor


Figure 4 Power requirements for varying operating conditions and different flight conditions versus

sampling frequency

Figure 5 Antenna dBm relative to receiver

detection lower threshold for 0°, 90°, 180°, and

270° orientations around the rod end stem at an

eight foot radius

Experimental Demonstration under Representative

Loading

An experimental demonstration of the wireless

load monitoring technology was conducted at the Lord

Corporation. The test set up is pictured in Figure 6. It was demonstrated that the wireless sensor system

reports the uni-axial rod end load time history in near

real time. The system consists of the piezoelectric

stack energy harvester, a 2.4 GHz wireless RF link,

and the magnetostrictive strain sensor to measure and

report the component load. Proper operation of all

three subsystems and their interoperability was tested.

The demonstration showed that for nine of twelve

typical loading conditions, the harvester provided

sufficient power to sample and transmit load data

wirelessly at a continuous rate of 100 Hz. Proper

operation of the wireless RF link was confirmed by the

receipt of full and coherent data sets. The strain sensor

operation was confirmed by registration of the rod end’s static and dynamic load levels. In Figure 7 and

Figure 8, force time histories are plotted as measured

by a calibrated load cell and KCF’s embedded load

cell for both sinusoidal and triangular force inputs. It

was observed that the two load cells agreed well over a

range of loading conditions.

On-going development work includes strain

sensor signal conditioning, increased strain sensor

sensitivity, data acquisition synchronization, storage of

data locally at the rod end, energy transmission to the

component when it is not loaded, wireless security,

improved energy harvester performance, and calibration modes of operation.

ACKNOWLEDGEMENTS

KCF would like to thank our technology

development partners, Lord Corporation, Sikorsky,

and Goodrich, for their continued support in this

development work.


Figure 6 Experimental testing of rod end with embedded components at Lord Corporation for varying

loading conditions at 4.3 Hz

7.1 7. 2 7.3 7. 4 7.5 7.6 7.7 7.8

-3000

-2000

-1000

0

1000

2000

3000

4000

Time (sec)

Ro

d E

nd F

orc

e (

lb.)

Lord Load Cell

KCF Rod End Load Sens or

Figure 7 Force time history of Lord load cell and

KCF’s embedded load cell for a sinusoidal force

input at 4.3 Hz

1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2. 2

-4000

-3000

-2000

-1000

0

1000

2000

3000

4000

5000

Tim e (sec)

Fo

rce

(lb

.)

Lord Load Cell

KCF Rod End Load Sensor

Figure 8 Force time history of Lord load cell and

KCF’s embedded load cell for a triangular force

input at 5 Hz


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