copyright © 2005 impact technologies, llc. all rights reserved. no further distribution is...

Copyright © 2005 Impact Technologies, LLC. All Rights Reserved. No further distribution is authorized without written permission.

Techniques and Engineering Software for Prognostics and Health Management of Flight

Control Actuators

Carl Byington, P.E. Matthew Watson

[email protected] [email protected]

2571 Park Center Blvd., State College, PA 16801

814-861-6273 www.impact-tek.com

Anthony PageNaval Air Warfare Center, Patuxent River

[email protected]

Presented at Aerospace Controls and Guidance Systems20 October 2005

Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.

Large Opportunity to Improve Actuator Maintenance in DoD

DoD – Navy, USAF, Army

Typically ~10-20 Actuators Per FW Aircraft~ 3600 U.S. Tactical Aircraft (NAVAIR/USAF)

F-14, F/A-18, F-15, F-16, and F-22 USAF: 4300 Fixed & Rotary Wing (1350 tactical)USA: Largest rotary wing user ~ 7000 helicopters

H-1, BlackHawk, Apache, Chinook, Kiowa, etc.JSF: 2,593 Planned + 2-3000 for exportUAVs (fixed, rotary, tilt) and Manned Tilt Wing (V-22) Ground-Mobile Combat Vehicles


A Picture of Actuator Health Management Today

3-Level Strategy Operational: On-board BIT which actuators to pull Intermediate: Test stand evaluation BIT fault detection Depot: Repair & Overhaul

BIT

Supply Warehouse

Repaired Units

Repair Depot

Test StandDiagnosed UnitsFaulty Units

Repaired/Validatedand CND Units

M/U andBIT Data

Data and Logs

MAF

O-Level I-Level

D-Level


Actuator Built-In-Tests (BIT)

Health monitoring of military actuators performed at O-Level and I-Level

O-Level employs Built-In-Tests (BITs) Apply conservative thresholds to identify

problems early and avoid in-flight failures Have witnessed high incidences of Can Not

Duplicate (CND) From BIT False Alarms, extreme operation, and

interdependent nature of military systems Results in high sparing requirements,

maintenance costs (parts and manning), and reduced operational readiness


Source: Bain, K. and Orwig, D. Presentation: “F/A-18E/F BUILT IN TEST (BIT) MATURATION PROCESS”, Presentation given October 10, 2000

BIT False Alarms

75% of CNDs caused by BIT False Alarms

>83% False Alarm Rate rate witnessed in some cases! Translates to MFHBFA

of 1 hr Leads to excessive

maintenance and reduced readiness 68% of O-level

maintenance from BIT


Improvements Needed at O-Level AND I-Level

Study evaluated F/A-18 A/B/C/D O-Level and I-Level wasted maintenance labor resulting from BIT false alarms during 1999CNDs resulted in Unnecessary Aircraft Downtime of 2.96

years Significant CNDs exist at both maintenance levels

Huge opportunity to recoup loses

Source: Bain, K. and Orwig, D. “F/A-18E/F Built-in-test (BIT) Maturation Process”. National Defense Industrial Association Systems Engineering Committee – 3rd Annual systems Engineering & Supportability Conference. 8/15/2000.


Actuator Health Classification and Prediction Methodology

Flight Control

Data

.

.

.

Servo Current

PositionCommand

RamPosition

HydraulicPressure

Fusion

FusedDamage

Level

n

jn

nn

fPfOP

fPfOPOfP

111

111

)()(

)()()(

Prognostics

RUL

Tfffx

t0

1.0

0

30%

Confidence

Good

Bad

“Graceful Degradation”Pro

gn

ostic C

on

fid

en

ce

Time

Com

po

ne

nt C

on

ditio

n

Impact Technologies, LLC

DamageLevel

ClassificationMode 1

Mode N

...

FeatureExtraction

Signal Processing

Neural Network

System ControlP * Kp

* KI

d/dt * KD

Features

Mo

de D

etect

Data

Mode

Data

Confidence

Classification

DamageLevel(PID)

Data Quality

Sample Characteristics Quality of Instance

Mode


Mode Detect and Fusion

Mode detect routine used to assess operation Random, quasi-steady, sinusoidal, dithering Feature response differences led to classifiers

trained for each mode Data quality estimate affects confidence

Fusion provides: More robust prediction Ability to interpolate between modes Diagnostic weights assigned based on mode


Feature Extraction: Neural Network Predicted Valve Position

Feed-forward time delay Neural Network used to predict healthy servo-valve position Servo current (mA), ram position command/response delta

(inches), and previous valve position (inches) Uses current value of each and 3 previous values Total of 12 inputs to the network

Predicted valve position is compared against measured position to create feature for classification

Input Layer with 3 Time Delays for each input

‘Linear’ Output Layer with 1 Neuron

Hidden Layer with 5 ‘Tansig’ Neurons


Feature Extraction: Dynamic Pressure Feature

High-frequency content (not just noise!) is good indicator of hydraulic system degradation Less affected by mechanical noise and biases More sensitive to signal changes during degradation

Frequency band selection: Above/between known natural/defect frequencies of system

(and harmonics) Analysis showed that valve outlet pressure is more

sensitive to changes in health than inlet pressure

Features

DynamicPressureFeature Vector

Processing

RawPressure

Signal


Health State Classification: Fuzzy Logic Classifier

Uses “partial truth” to classify health based on features

Assigns “degree of membership” in membership functions of fuzzy system

Rule-base interprets “degree of membership” of each input to determine classification Uses engineering

knowledgeExplainable to end user

Readily produces health (1-0) or damage index (0-1) indicative of current health

HighLowMedium

Input Membership Functions

Automated Fuzzy Inference of Health State

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Output Health Classification

Fuzzy Logic Rule-Base

FeatureInputs

Damage IndexOutput


F/A 18 Stabilator: Electrohydrostatic Valve Degradation

Method implemented to predict degradation of F/A-18 Stabilator Electrohydrostatic Valves (2 EHSVs per Actuator)

Data provided by Boeing from F/A 18 Stabilator Test Stand:1. Healthy EHSVs with degradation simulated by placing

electromagnet in close proximity to EHV

2. Faulty EHSVs removed from service (from North Island depot)

3. EHSV seeded with scored shuttle spool

Courtesy ofCourtesy of

Sys 1 EHVS/N XXXX Stabilator Actuator

Sys 2 EHV


Fault-to-Failure Prediction :Assessing Ground Truth

Automated health assessment implemented using damage index Translation of capability to health state determined by end-user

or designer Damage index in current EHSV data evaluated by Impact

Using test documentation from Boeing

Likelihood of Intermittent Failure BIT Results EM Simulated Degradatation

Known Good Unit100% FunctionalNominal Unit

EM Off 0

Unit Not likely to CauseIntermittent Failures

Fails Test BenchPasses IBIT and PBIT

EM 20% current 0.25

Unit May CauseIntermittent Failures

Fails IBITPasses PBIT

EM 40% current 0.5

Unit Likely to CauseIntermittent Failures

Fails IBIT and PBIT EM 60% current 0.75

-------------------------------- ----------------- EM 80% w/ Reset, EM 80% 0.85

Completely Failed UnitHard FailureWill not Function

EM 80%, 100% current 1

Boeing Ground Truth Information Impact Assessment of

Damage


Feature Extraction Results: Neural Network Predicted Valve Position

NN predictor error

feature computed at

multiple time windows Error feature clearly

tracks with EM

degradation

0% EM

40% EM

80% EM (Failed)

AfterReset

Po

siti

on

Err

or

[in

2]


Feature Extraction Results: Dynamic Pressure Feature

Higher frequency

energy provides

repeatable increases Dynamic pressure

feature clearly tracks

with degradation

0% EM

40% EM

80% EM (Failed)


Health State Prediction Results : Fuzzy Logic Classification EM Degradation

4 % error across 106 classifications

Classification results from Run # 3 (Mode 1), time window: 125-130 seconds

Mode #Ground Truth

DamageAve. Error

[%]Std. Error

[%]

0 4.13 1.34

0.25 2.21 4.06

0.5 4.19 4.91

0.75 6.52 4.65

1 2.89 1.04

0 2.84 0.23

0.2 1.50 2.51

0.4 0.02 0.00

0.6 0.13 0.09

0.8 0.06 0.03

1 4.90 3.01

0 10.44 8.32

0.2 4.02 6.45

0.4 7.07 5.72

0.6 9.28 7.80

0.8 4.28 2.24

1 6.84 1.95

4.20 3.20

Mode 1 - Dither

(Run #1-3)

Classification Error

Average Results:

Mode 2 - 0.17 Hz Sinusoid

(Run #4-6)

Mode 3 -0.08 Hz Sinusoid

(Run #7-9)

Ground Truth Information


Health State Prediction Results : Fuzzy Logic Classification of Returned EHSV

Classification of EHSV removed from field service

Used classifier trained on EM simulated fault data

Ground truth assessment more subjective

Sys 2 (Rod End) Valve Serial # File NameDamage

Classification

Estimated Ground Truth Damage

Level

r053 0.02

r054 0.03

r055 0.02

r056 0.02

r038 0.62

r039 0.50

r044 0.98

r045 0.98

r156 0.86

r157 0.76

XXXX(EHV Failed)

1.00

XXXX (Unit May Cause Intermittent Failures)

0.50

XXXX(Scored Shuttle Spool)

0.85

Classification Results on Used Valves with Known Level of Degradation

XXXX(Known Good Unit)

0.01

XXXX(Known Good Unit)

0.01


Physical Modeling and Parameter Estimation

Actual system response is a result of nominal system response plus faults and uncertainty

Physical model used to simulate actuator response Model parameters updated recursively to match model output

with actual response Used as indicators of system health

Optimization routine used to determine ‘best fit’ model parameters

Model

System

Damage Estimator

Measured Input

Measured System Output

Output Residual + Degradation ID

-

+

+

+

Fault Effects & Variation Uncertainty


Hardware-in-the-Loop Analysis and Demonstration

Data collected on Moog EMA * Industrial 3-Ton EMA

dSpace* used for control and command/response data collection (5000 Hz) Motor Velocity Motor Torque Actuator Velocity Actuator Position

Collected: Baseline data Multiple severity fault

data

Courtesy of


Fault Seeding Gear Fault

Gear slipping simulated by altering command signal

Areas where slipping occurs results in command of zero Energy Conversion

“Dead Zones” Fault simulated using

SIMULINK block To modify command

signal

MotorPosition(Radians)

2π

π

23π

0

BrokenTooth

“DeadZone”

SIMULINK Fault Simulation Block


Fault Seeding Bearing Fault

EMA FMECA* analysis: Failure rate, Prob. Of

Occurrence, Severity Gear and bearing

problems most critical EMA modified for simulation

of bearing seizure Friction increases as

seizure progresses Add friction by clamping

on output shaft Controlled by tightening

friction screw

Courtesy of Courtesy of

BearingSeizure


Model-Based Fault-to-Failure Prediction: EMA Data Table

Seeded FaultFault

SeverityAmplitude

[Hz]Frequency

[Hz]

1 Bearing Seizure 0-70 ft-lbf* 0.1 0.2














15 Gear Slip 0 - 50%** 0.1 0.2

16 Gear Slip 1 - 50%** 0.1 0.2

17 Gear Slip 2 - 50%** 0.1 0.3

18 Gear Slip 3 - 50%** 0.1 0.3

19 Gear Slip 4 - 50%** 0.1 0.4

20 Gear Slip 5 - 50%** 0.1 0.4

* Data collected every 10 ft-lbf** Data collected every 10 %

Position Command (Sine Wave)

Run #

Fault Information

20 fault-to-failure data runs collected

160 data snap-shots Sine wave position

command 2 amplitudes 3 frequencies

Bearing seizure FM Snap shots in 10 ft-

lbf increments Gear slip FM

Snap-shots in 10% increments


System Model and Parameters

MATLAB Simulink® model of EMA


ElectroMechanical Actuator( EMA) Model Parameters to Faults

Four parameters identified to diagnose FMs of interest: Frictional Damping Coefficient [in-lbf-sec/rad] Local Gear Stiffness [in-lbf] Torque Constant [in-lbf/Amp] Motor Temperature [degF]

Gear slipping Decrease in Local Gear Stiffness Small increase in Frictional Damping Coefficient

Bearing seizure Large increase in Frictional Damping Coefficient Small increase in Motor Temperature

Motor Failure Decrease in Torque Constant Large increase in Motor Temperature


-4

-3

-2

-1

0

00.25

0.50.75

11.25

1.5

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

Torque Constant

Failure Progression in Scalar Space

Frictional Damping

Lo

ca

l G

ea

r S

tiff

ne

ss

Failure Progression

Normal

Gear Slip

Bearing Seizure

Motor Failure

1.0

0.5

1.0

0.5

1.0

0.5

Frictional Damping

Lo

cal

Gea

r S

tiff

nes

s

Model-Based Fault-to-Failure Prediction: Probabilistic Health Classification

CurrentValues

Proximity Likelihood

Severity Euclidean distance between current state and the fault region becomes gradually smaller as the system degrades

Projections evaluated in feature space to predict the usage time when “current condition” reaches functional failure region


Separable Fault Classes Through PCA


Model-Based Severity Classification

Overall error was about 3% for both failure modes with very repeatable results

Seeded Fault Test

Actual Fault

Severity

Predicted Severity

(Average)+/-

AverageError

OverallError

0.000 0.039 0.034 3.95%

0.125 0.169 0.058 4.45%

0.250 0.265 0.078 1.54%

0.375 0.345 0.066 3.00%

0.500 0.427 0.070 7.32%

0.625 0.591 0.170 3.41%

0.750 0.735 0.170 1.53%

0.875 0.850 0.122 2.48%

1.000 0.991 0.000 0.94%

0.000 0.027 0.037 2.67%

0.200 0.113 0.054 8.69%

0.400 0.360 0.134 3.96%

0.600 0.603 0.068 0.28%

0.800 0.790 0.062 1.03%

1.000 0.959 0.032 4.06%

Bearing Seizure

GearSlip

3.18%

3.45%


Model-based EMA PHM Demonstration


Implementation Options and Technical Approaches


Servocylinder Test Stand Data Use Summary

Data-Driven Approach Servo-current command needed to compute electric current

signature analysis feature Pressure measurements will be used to generate dynamic

pressure feature Load and duty cycle measurement will be used in mode detect

algorithm

Model-based approach Pressure, flow, load and position measurements will be used to

match model response to actual system response To enable autonomous model parameter identification

Additional control parameters are needed in both cases so that desired response is known If included, sensor suite is sufficient to implement both data driven

and model-based approaches


Actuator Test Stand CAHM™

Features Windows-based tool Autonomous data capture &

archiving Discrepancy analysis Rogue unit & fleet analysis Applicable to Hydraulic, EHAS,

& EMA systems

Benefits Significant reduction in CNDs and life cycle costs

Detect early fault conditions via gray-scale health Detect maintenance-induced failures Identify rogue units

More intelligent aircraft maintenance and overhaul


Continuing Work

Improved search method

Additional failure mode validation

Additional reasoning levels with Health Index

Prognostic algorithm

Transition to embedded controllers


Additional Technology Transitions

JSF Propulsion System Actuators

UAV HUMS

JSF STOVL 3BSM Actuators

F/A-18 C/D CAHM™ Software

•Data Driven PHM•Model-Based PHM•Data Fusion•Prognostics


Actuator PHM Summary

Data-driven PHM effective and does not require extensive modeling of system Algorithm demonstrated high accuracy and repeatability on F/A-18

C/D Stabilator EHSV data No new sensors - provides easier forward-fit (& retro-fit) NN based method has determinism issues for on-board

Model-based approach provides greater diagnostic value but much higher computational complexity Demonstrated with hardware-in-the-loop EMA with gear and bearing

faults in multi-state space Required no additional sensors but need more processing Reduced order models may enable on-board use

CAHM™ software being developed and demonstrated to improve STS test program and reduce CNDs

Software can aid transition to 2-level maintenance Approach being be adapted and evaluated for other

types of actuator systems


Thanks and Acknowledgment

NAVAIR Program Support Anthony Page Karine Mouradian Marc Steinberg

Boeing personnel for test data & engineering insight Kirby Keller Martin Eis Kevin Swearingen John Vian

Thank you and Questions…

copyright © 2005 impact technologies, llc. all rights reserved. no further distribution is...

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