Aviation Safety Program Integrated Vehicle Health Management (Srivastava).pdf · Section D5:...
Transcript of Aviation Safety Program Integrated Vehicle Health Management (Srivastava).pdf · Section D5:...
National Aeronautics and Space Administration
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National Research CouncilAssessment of NASA’s Aeronautics Research
Program
Aviation Safety ProgramIntegrated Vehicle Health Management
Principal Investigator: Ashok N. Srivastava, Ph.D.Project Manager: Joseph E. Grady, Ph.D.
Associate Project Manager: Susan Johnson
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National Aeronautics and Space Administration
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AgendaProject Goals and Objectives
Project Structure and Milestones
Program Coordination and Partnership
NASA IVHM Relationship to the Decadal Survey
Future Work
The IVHM Data Mining Lab
National Aeronautics and Space Administration
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Project Goals and Objectives
Aviation Safety Program GoalsDevelop technologies, tools, and methods to:
- Improve aircraft safety for current and future aircraft
- Overcome safety technology barriers that would otherwise constrain full realization of the Next Generation Air Transportation System
- Concurrently, leverage these technologies to support space exploration activities, such as enabling self-reliant and intelligent systems necessary for the long-duration travel requirements of future space vehicles.
IVHM Project GoalsDevelop technologies to determine system/component degradation and damage early enough to prevent or gracefully recover from in-flight failures in both the near-future and next-generation air transportation systems, and to provide enabling technology for space exploration.
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http://www.nasa.gov/pdf/168652main_NASA_FY08_Budget_Request.pdf (ARMD-12)
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IVHM Project Goals
National Aeronautics and Space Administration
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Project Goals and Objectives
Project Structure and Milestones
Program Coordination and Partnership
NASA IVHM Relationship to the Decadal Survey
Future Work
The IVHM Data Mining Lab
National Aeronautics and Space Administration
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National Aeronautics and Space Administration
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Selected IVHM Milestones
National Aeronautics and Space Administration
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AgendaProject Goals and Objectives
Project Structure and Milestones
Program Coordination and Partnership
NASA IVHM Relationship to the Decadal Survey
Future Work
The IVHM Data Mining Lab
National Aeronautics and Space Administration
www.nasa.gov
Cross-Project and Program Coordination
Project or Program Activity
IRAC C-17, F/A-18, Airstar shared testbedSystems Analysis
IIFD Architecture analysisData mining for cockpit switching
AAD Crack propagation, aging and durability tools, providing SHM data
Space Operations Mission Directorate Virtual Sensors and anomaly detection methods for ISS and Shuttle
Science Mission Directorate Virtual Sensors for anomaly detection
Exploration Systems Anomaly detection algorithms for Ares
Air Force NASA-AF Executive Research Committee
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New Space Act Agreements: • FLX Micro: to develop a high temperature, wireless pressure sensor • Moog: to develop prognostic technologies for electromechanical actuators • Boeing: to develop data mining technologies for anomaly detection
Developing Space Act and Other Partnership Agreements: • Sun Microsystems: Sunspot wireless sensors for prognostics • Sikorsky: Testing and integration of Sunspot wireless sensors• Lockheed Martin: Data mining anomaly detection methods on an F-16.
Partnership Status IVHM4Info%
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Moog SAA• Prognostics for Electro Mechanical Actuator (EMA) Systems• Moog Activities
– Moog has a set of 40+ EMAs dedicated for this work (power rating in aircraft actuator range)
– Provide guidance on developing new and/or simplified models for EMAs– Assist in the EMA prognostic algorithm verification and validation via tests with seeded
faults– Data will be shared with NASA
• NASA Activities– Develop physics-based models for actuator damage propagation
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Design OutFail / OperateJam
Wall Thickness / Structural
Screw Fracture
Core Ductility
Load Distribution
Thread Shear
Contact Fatigue
Brinelling
False Brinelling
Lubrication
Misalignment
Classical Brg Race Failures
Screw
Circuit Orientation
Wall Thickness / Structural
Nut Fracture
Core Ductility
Load Distribution
Thread Shear
Contact Fatigue
Brinelling
False Brinelling
Lubrication
Misalignment
Classical Brg Race Failures
Nut
Great Circle Generation
Ball Fracture
Dimpling
Wear
Classical Brg Ball Failures
Ball
Fatigue Damage
Wear Damage
Misaligment
Rupture
Return Failure
Ice Damming
External
Internal
Contamination
Screw / Nut Seperation
Return
Circuit
Ball Screw Failures
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IVHM4Info%
AgendaProject Goals and Objectives
Project Structure and Milestones
Program Coordination and Partnership
NASA IVHM Relationship to the Decadal Survey
Future Work
The IVHM Data Mining Lab
National Aeronautics and Space Administration
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Section D5: Fault-tolerant and Integrated Vehicle Health Management systems
“Fault-tolerant aircraft systems, coupled with CBM, may improve aircraft safety and reduce aircraft life-cycle maintenance and ownership costs. Critical research tasks include developing:
1. Robust and reliable hardware and software tools for monitoring components, detecting faults, and identifying anomalies;
2. Prognosis analysis tools for predicting the remaining life of key components;
3. Approaches for recovering from detected faults, including reconfiguration of the flight control system for in-flight failures of manned and unmanned aircraft; and
4. Low-cost, lightweight, wireless, self-powered sensors with greater memory and processing capability.”
From the Decadal Survey of Civil Aeronautics.
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Rotordynamic AnalysisNASA Decadal Survey: D5 and C1
• Development of a physics based diagnostic system
• Disk imbalance causes synchronous whirl
• Current model accounts for shifting eccentricity as function of crack characteristics and speed
• Notch used to emulate crack for global, vibration response
x
y
z
T
Machined flaw
Milestone 1.3.1
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Modeling Approach
Equations of motion Solution for undamaged case (fixed imbalance)
( )θω −= tXx cos
( )θω −= tYy sin
( ) ( )222
2
21 rr
erYXξ+−
==
⎟⎠⎞
⎜⎝⎛
−= −
21
12tan
rrξθ
r = ω/ωn
{
These terms define the synchronous whirl amplitude.
texxx nn ωωωξω cos2 22 =++ &&&
teyyy nn ωωωξω sin2 22 =++ &&&
x
y
O
C
Me
θU
V
6/4/2007
Modeling Approach
Modeling the crack dependenceTotal eccentricity is a function of crack characteristics and speedSum of initial imbalance vector and imbalance due to crack
e0
eecr
x
y
ecr sin ββα
e0 +ecr cos β
βcos2 022
0 crcr eeeee ++=
ββ
αcos
sintan
0
1
cr
cr
eee
e+
==∠ −
Crack dependent solution
( ) ( )( )( )αθω
ξ
β+−
+−
++= t
rr
eeeerx crcr cos
21
cos2222
022
02
( ) ( )( )( )αθω
ξ
β+−
+−
++= t
rr
eeeery crcr sin
21
cos2222
022
02
( ) ⎟⎟⎠
⎞⎜⎜⎝
⎛+
+⎟⎠⎞
⎜⎝⎛−
=+ −−
ββξαθ
cossin
tan1
2tan0
12
1
cr
cr
eee
rr
Phase angle has new term due to the introduction of a crackAmplitude of ecr is defined by numerical analysis
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ResultsNo notch
1.2 in notch at 0 degrees
1.2 in notch at 90 degrees
1.2 in notch at 180 degrees
1.2 in notch at 270 degrees
0
50
100
150
200
250
0 5000 10000 15000Shaft speed, rpm
Phas
e, d
egre
es
0
0.0005
0.001
0.0015
0.002
0.0025
0 5000 10000 15000Shaft speed, rpm
Sync
hron
ous
whi
rl am
plitu
de, i
n
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ADAPT Advanced Diagnostics and Prognostics Testbed
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• Provide development platform with real, representative Electrical Power System (EPS) components for: – Diagnostic and prognostic models and algorithms– Advanced caution and warning methods
• Allow performance assessment of these algorithms– Standardized testbed– Repeatable failure scenarios
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Preliminary Characterization of Sensor Placement Methodology
Iterative Down-Select ProcessIterative Down-Select Process
Knowledge BaseKnowledge Base
Final SelectionFinal Selection
HealthRelated
Information
HealthRelated
Information
SystemSimulation
SystemSimulation
Down-SelectAlgorithm
(Genetic Algorithm)
Down-SelectAlgorithm
(Genetic Algorithm)
SystemDiagnostic
Model
SystemDiagnostic
Model
Sensor SuiteMerit
Algorithm
Sensor SuiteMerit
Algorithm
OptimalSensorSuite
OptimalSensorSuite
Candidate Sensor Suites
i=0 i>0
Candidate SelectionComplete
YesNo
Collection of NearlyOptimalSensorSuites
StatisticalEvaluationAlgorithm
StatisticalEvaluationAlgorithm
CombustorFan
LPC HPC LPTHPT
Blue = typical sensors
Green = optional sensors
Red = advanced sensors
FADECControl Sensors
T2AP2A
T25 T3PS3 WF36 XN2 XN25T49
OptionalSensors
P17T17
P25
AdvancedSensors
T5P5
T41P41 P48W17 W25 W3 W49 W5W41
Large Commercial Turbofan Engine – Problem Characterization
Systematic Sensor Selection Strategy (S4) ArchitectureNASA Decadal Survey: D5 and B3
The overall fitness of different sensor suites selected by the S4 algorithm
Sensor Selection Results:
Target Num
Target Wt. XN2 XN25 P2A T2A P17 T17 P25 T25 PS3 T3 T49 P5 T5 WF36 Fitness
Num Sensors
9 0.000 X X X X X X X X X X X X 27.708 129 0.010 X X X X X X X X X X X X 26.901 129 0.100 X X X X X X X X X X 23.161 109 0.500 X X X X X X X X X 20.857 910 0.001 X X X X X X X X X X X X 27.653 12
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Comparison of Unsupervised Anomaly Detection Methods, JANNAF ’07Outstanding Achievement in Liquid Propulsion Award in the Operational Systems category at the JANNAF 3rd LPS Meeting in May, 2007
NASA/Decadal Survey D5 and B3
N/A130.54553.05277.84295.8467.04130Turbine Blade Failure
A10853
N/A156.64N/A183.4431.4420.66119Knife Edge Seal CrackA20619
11.8417.4423.4423.44176.2423.4411.62
Fuel Leak and
Controller Failure
STS-93 (#3)
12.64N/A25.0425.0415.4425.0411.38Controller Failure
STS-93 (#1)
N/AN/AN/AN/AN/A77.8232.76Sensor Failure
STS-91 (#1)
N/AN/AN/AN/AN/AN/A74.42
Anomalous Spike in Sensor
Reading
STS-77 (#2)
GMMLDSIMSSVMGritBotOrcaActual
Time of failure (in seconds) as recorded by each methodFailure Type
Failure Data
Milestone 1.6.1Blue: Fastest response times. Red: Highest accuracy. 20
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Section D5 cont’d: Fault-tolerant and Integrated Vehicle Health Management systems
“Evaluate component capability in a simulated environment(ground test and hardware in the loop). That is, take a particular subsystem, such as a real landing gearsystem that has been represented by an appropriate behavioral model as specified and insert simulated faults to test for proper operation of the health monitoring system.”
From the Decadal Survey of Civil Aeronautics.
National Aeronautics and Space Administration
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727 Landing Gear Test Preparations in ALDF
Description: The Aircraft Landing Dynamics Facility (ALDF) is capable of exercising a landing gear, for a brief extent, through the touchdown, rollout, and braking phase of aircraft landings at operational load and speed conditions while injecting subsystem faults. Data collected during the tests can be used to develop and verify diagnosis andprognosis algorithms. Preparations are underway for a series of 727 main landing gear tests in the ALDF. Over ten test runs are planned, including fault injections in the hydraulic system, anti-skid valves, brakes, and wheel speed sensors.
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Section C1: Integrated Vehicle Health Management“IVHM holds the promise of reducing vehicle cost,weight, and maintenance downtime, as well as speeding theintroduction of new material systems and structural concepts.Real-time onboard sensor systems that monitor the actualstate of materials and structural components enable moreefficient use of material, including novel concepts…”
“With a better model of itself, the aircraft can trace back system anomalies through the multitude of discrete state and mode changes to isolate aberrant behavior. Fault-tolerant systems combine simple rule-based reasoning, state charts, model-free monitoring of cross-correlations among state variables, and model-based representations of aircraft subsystems.”
From the Decadal Survey of Civil Aeronautics.
National Aeronautics and Space Administration
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Classification of Damage Signatures in Composite Plates Using One-Class SVMs, submitted to AIAA Journal
a
b
c 0.4
0.5
0.6
0.7
0.8
0.9
1
30 40 50 60 70 80 90
100
Torque
Cla
ssifi
catio
n R
ate
Milestone 1.6.1
National Aeronautics and Space Administration
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AgendaProject Goals and Objectives
Project Structure and Milestones
Program Coordination and Partnership
NASA IVHM Relationship to the Decadal Survey
Future Work
The IVHM Data Mining Lab
National Aeronautics and Space Administration
www.nasa.gov
• Research and development of diagnostic and prognostic methods across structures, propulsion, and aircraft systems.
• Release of NASA Research Announcement.
• Fostering cross agency technology advancements to benefit IVHM.
• Data Mining in Aeronautics Science and Exploration Systems conference
• Building an IVHM Data Mining Lab
• Developing publicly available IVHM data.
Future Work
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National Aeronautics and Space Administration
www.nasa.gov
AgendaProject Goals and Objectives
Project Structure and Milestones
Program Coordination and Partnership
NASA IVHM Relationship to the Decadal Survey
Future Work
The IVHM Data Mining Lab
National Aeronautics and Space Administration
www.nasa.gov
Mission of the IVHM Data Mining Lab
The lab enables the dissemination of Integrated Vehicle Health Management data, algorithms, and results to the public. It will serve as a national asset for research and development of discovery algorithms for detection, diagnosis, prognosis, and prediction for NASA missions.
Serves several High Priority R&T Challenges including C1, D5, E8B, and E8C.
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Features of the IVHM Data Mining Lab
Datasets• Propulsion, structures, simulation
and modeling• ADAPT Lab • Icing• Electrical Power Systems• Systems Analysis • Flight and subscale systems• Fleet-wide data• Multi-carrier data
Open Source• Code• Papers• Generation of an IVHM community
Selected Discovery Tools• Inductive Monitoring System
(IMS) – cluster-based anomaly detection
• Mariana – Text classification algorithm
• Orca – Distance-based outlier detection
• ReADS – Recurring anomaly detection system for text
• sequenceMiner – anomaly detection for discrete state and mode changes in massive data sets.
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Key Research Issues Addressed in the IVHM Data Mining Lab
• Real-time anomaly detection • Model-free prediction methods• Hybrid methods that combine discrete and continuous data• Distributed and privacy-preserving data mining• Analysis of integrated systems
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Appendix
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DATA
IVHM Data Mining LabThere is a suite of tools already available for analysis, so researchers will not only be able to help analyze the data now, but the additional data will allow for continued R&D to develop algorithms and tools to answer evolving and complex questions (such as system-level questions).
IVHM elements create large amounts of data
The results of the data mining analysis, simulated data sets and new tools could be returned to the other research areas as well as disseminated to other NASA directorates, academia etc.., creating a mutually beneficial relationship of enhanced data analysis.
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From the Decadal Survey….Section B9
“The growth of new MEA loads is being driven byadvances in the capabilities of electric actuators and controls,and it is being enabled by the development of moreflexible and reliable aircraft generators.”
“Intelligent power management and distribution (PMAD)using advanced system models and wireless sensors orSensor-less control technologies for graceful degradationand failsafe operation.”
From the Decadal Survey of Civil Aeronautics.
National Aeronautics and Space Administration
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Diagnosing Electrical Power Systems Using Bayesian NetworksNASA/Decadal Survey: B9 and D5
BN Inference
Command nodes
Health nodes
Sensor nodes
INPUT: OUTPUT:
Description:Bayesian networks offer an effective way to distinguish sensor faults or noise from component or system faults. They can model probabilistic, uncertain behavior but also exploit determinism in the system. For example, in circuit analysis, deterministic behavior takes the form of Ohm's law and Kirchhoff's laws. The goal of this effort is to develop a sensor validation approach that is powerful in domains with a combination of probabilistic and deterministic behavior; an example is the ADAPT lab.
To Battery
To Load
Outcome/Results:Professor Darwiche and students from UCLA visited the ADAPT lab to continue collaborations with NASA researchers on the development of an approach for modeling both probabilistic and deterministic behavior by means of Bayesian networks. The approach involves the generation of a Bayesian network with the following types of nodes: • Health nodes
– System health: represent health of system – Sensor health: represent health of sensors
• Command nodes: commands from a testbed user • Sensor nodes: read from ADAPT or from vehicle • Push nodes: mediate flow being pushed from battery to load• Pull nodes: mediate flow being pulled by load from battery
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