copyright © 2005 impact technologies, llc. all rights reserved. no further distribution is...
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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
carl.byington@impact-tek.com matthew.watson@impact-tek.com
2571 Park Center Blvd., State College, PA 16801
814-861-6273 www.impact-tek.com
Anthony PageNaval Air Warfare Center, Patuxent River
anthony.page@navy.mil
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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.
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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]
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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)
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
2 Bearing Seizure 0-70 ft-lbf* 0.1 0.2
3 Bearing Seizure 0-80 ft-lbf* 0.1 0.2
4 Bearing Seizure 0-80 ft-lbf* 0.1 0.3
5 Bearing Seizure 0-80 ft-lbf* 0.1 0.3
6 Bearing Seizure 0-80 ft-lbf* 0.1 0.4
7 Bearing Seizure 0-80 ft-lbf* 0.1 0.4
8 Bearing Seizure 0-80 ft-lbf* 0.2 0.2
9 Bearing Seizure 0-80 ft-lbf* 0.2 0.2
10 Bearing Seizure 0-80 ft-lbf* 0.2 0.3
11 Bearing Seizure 0-80 ft-lbf* 0.2 0.3
12 Bearing Seizure 0-80 ft-lbf* 0.2 0.4
13 Bearing Seizure 0-80 ft-lbf* 0.2 0.4
14 Bearing Seizure 0-80 ft-lbf* 0.1 0.3
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
System Model and Parameters
MATLAB Simulink® model of EMA
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
-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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
Separable Fault Classes Through PCA
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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%
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Model-based EMA PHM Demonstration
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Implementation Options and Technical Approaches
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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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
Continuing Work
Improved search method
Additional failure mode validation
Additional reasoning levels with Health Index
Prognostic algorithm
Transition to embedded controllers
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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
Copyright © 2005 Impact Technologies, LLC. All Rights Reserved.
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…
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