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University of MarylandCopyright © 2009 CALCE

1calceTM 1Prognostics and Health Management Group

Prognostics Implementation in Aerospace Applications

PrognosticsTM

Michael Pecht, Ph.D.CALCE Electronic Products and Systems

University of Maryland – USA

Prognostics and Health Management, Condition-Based Maintenance and Health

& Usage Monitoring Symposium21-22 April, 2009



• Formally started in 1984, with support from NSF, as a US Center of Excellence in electronics systems reliability.

• Over $6.5M in funding per year, by over 150 of the world’s leading electronics organizations

• One of the world’s most advanced and comprehensive electronics testing and failure analysis laboratories

• Supported by 112 faculty, visiting scientists and research assistants

A Brief History of CALCE


Prognostics and Health Management 3

• Alcatel-Lucent• ALZA (Johnson&Johnson)• Amkor• Arbitron• Arcelik• ASC Capacitors• ASE• Arbitron• Astronautics• Atlantic Inertial Systems• AVI-Inc• Axsys Engineering• Battelle • Branson Ultrasonics• Brooks Instruments• Capricorn Pharma• Cascade Engineering • AMSAA Reliability Branch• Boeing• BAE• CAPE – China• CDI• Cisco Systems, Inc.• Crane Aerospace & Electronics• Curtis Wright/Dy4 De Brauw

Blackstone Westbroek• Defense Micro Electronics

Activity• Dell Computer Corp.• EIT, Inc.• Embedded Computing &

Power• EMCORE Corporation

• EADS – AirBus• Emerson Advanced Design• Emerson Appliance Controls• Emerson Appliance Solutions• Emerson Electric Co.• Emerson Network Power• Emerson Process Management• Ericcson• DRS EW Network Systems,

Inc.• Essex Corporation • Exponent, Inc.• Fairchild Controls Corp.• Filtronic Comtek• GE Fanuc Embedded Systems• GE Global Research• General Dynamics – AIS• General Motors• Guideline• Hamlin Electronics Europe• Hamilton Sundstran• Harris Corp• Honda• Honeywell• Howrey, LLP• Huawei• Intel• Juniper• Kimball Electronics• L-3 Communication Systems

• LaBarge, Inc• Laird Technologies • Liebert Power and Cooling• LM Aero - Ft. Worth Site• Lockheed Martin Lutron

Electronics • Maxion Technologies, Inc.• Motorola• Joint Strike Fighter

Program• Mobile Digital Systems, Inc.• n-Code• NASA Goddard Space

Flight• NetApp• Nokia• Northrop Grumman• NXP Semiconductors• Ortho-Clinical Diagnostics• PEO Integrated Warfare• Petra Solar • Philips• Philips Medical Systems• Pole Zero Corporation• Pressure Biosciences• Raytheon • Rendell Sales Company• Research in Motion• RNT, Inc.• Rolls Royce• Rockwell Automation

• Samsung Memory• Samsung Techwin• S.C. Johnson Wax• SanDisk• Schlumberger• Schweitzer Engineering Labs • Sensors for Medicine and

Science, Inc.• SiliconExpert• SolarEdge Technologies• Space Systems Loral• Starkey Laboratories, Inc• Sun Microsystems• Symbol Technologies, Inc• Team Pacific Corporation• Tech Film• Tekelec• Teradyne• The Bergquist Company• The M&T Company• The University of Michigan• Tin Technology Inc• TruePosition, Inc.• TÜBİTAK Space Technologies• Vectron International, LLC• Weil, Gotshal & Manges LLP• Whirlpool Corporation• WiSpry, Inc.• Woodward Governor

Organizations that Fund CALCE: $6.5M in 2008



RAMS Challenges in Aerospace

• Key systems are now controlled by a complex network of electronics– Guidance, Navigation, and Control– Communications and Tracking– Sensors and Instrumentation– Electrical Power Systems– Data Processors– Data Busses/Networks



Challenges for Avionics Industry

• COTS components• Rapidly changing technology / faster obsolescence • Imperfect screening and qualification standards• Intermittent failures• Significant numbers field failures turn out to be NTF• Redundant systems• Scheduled maintenance


6calceTM 6Prognostics and Health Management

Environmental Demands on Avionic Systems• high temperatures: 110 °C for 2000 h

• low temperatures: -60 °C for 100 h / -55°C 200 h

• temperature cycle: -40 °C / 110 °C 2000 h

• temperature cycle (slow):

-40/+85 °C, soak time Tmin 90 min, Tmax 150 min, tchange 150 min

• temperature shock: -40/+85 °C, soak time Tmax 30 min, Tmin 45 min, tchange 10 s

• temperature storage: 85 °C, 680 h

• relative humidity (cyclic):

55 °C 93% r.h, 25 °C 95 % r.h., cycle duration 4 h, 6 days

• low pressure test: 91,2 mBar 100 h

• radiation Xenon-light:

3000 h

• radiation UV-light: 3000 h



Worst CaseUse Conditions Typical Use Conditions

Use categoryMin temp (°C)

Max temp (°C)

Service life

(years)

Tmin(°C)

Tmax(°C)

ΔT (°C) Hours Yearly

cycles

Consumer 0 60 1-3 20 55 35 12 365Computer 15 60 5 25 45 20 2 1460Telecom -40 85 7-20 10 45 35 12 365Commercial aircraft -55 95 20 20 40 20 12 365Industrial/ auto -55 95 10 30 50 20 12 185Military ground/ship -55 95 10 5 45 40 12 100Space -55 95 5-30 20 55 35 1 8760Military avionics -55 95 10 0 80 80 2 365Auto (under hood) -55 125 5 20 80 60 1 1000IPC-SM-785, Guidelines for Accelerated Reliability Testing of Surface Mount Solder Attachments, November 1992.

Beware of “Standard” Profiles



Instrument Panel

EE Bay Air

EE Bay (AEM)*Console (AEM)*

Dallas San Diego

Ren

oPhoenix

Vega

sR

eno

Vega

s

SMF

Phx Milwaukee Phx

60

50

40

30

20

10

0

-10

0.00

3.59

5.58

7.57

9.56

11.5

513

.54

2.00

15.5

3

19.5

121

.50

23.4

91.

483.

475.

46

17.5

2

7.45

11.4

313

.42

15.4

117

.40

19.3

921

.38

9.44

Time (hh.mm)

Tem

pera

ture

(°C

)AvionicsFan

Jet Engines

Example: Aircraft Temperature Profile

Cluff, K., Barker, D., Robbins, D., and Edwards, T., “Characterizing the Commercial Avionics Thermal Environment for Field Reliability Assessment,” Journal of the IES, Vol. 40, No. 4, pp. 22-28, 1997.

* Measured by an Aircraft Equipment Monitor (AEM) fitted in a commercial aircraft.



EADS HUMS and CALCE Prognostics ProjectMicrocontrollerTemperature & RH sensor Terminals for data

communication

FRAM memoryTerminals for external sensors

Terminals for battery

2-axis accelerometer

RFID

Courtesy of EADS



CALCE E-Prognostics Sensor Tag



Inspection Intervals• IL-Check - 48 month

– Detailed inspection of structure, fuselage and wings. – Check and maintenance of electronic and hydraulic systems – Implementation of improvements, complete cabin

maintenance• D-Check - 72 month

– General maintenance, check of panels, bolts, screws for fatigue or wear out.

– Replacement of bigger parts, – Replacements of all instruments and devices, – Duration: 4-6 week, extend: 30,000- 50,000 working hours



PHM Challenges in Avionics

• The cost of maintenance is already over 15% of the total operation costs

• System / component faults and failures are very difficult to detect, diagnose, and mitigate in-flight with existing technologies.

• Need Prognostics and Health Management (PHM)



Prognostics-Based Logistics Demo: F/A-18

LRU with PHM Sensor Module and RFID

Mission data and id

Net-Centric logistics database

Smart Reader/Writer

Logistics Planner

LRU history and prognostics

Queries and updates for logistics decisions

Updated logistics / maintenance history and id

In 2005 UMD team was awarded $2.1M from US OSD to develop an interactive supply chain that is based on prognostics, RFID, and network databases



NASA Contract: Reliable Diagnostics and Prognostics for Critical Avionic Systems

• Project objectives include:– Develop approaches to detect faults, to model degradations,

and to predict failures in avionics components. – Develop a methodology involving parameter selection, feature

extraction, pattern recognition, anomaly detection, parameter isolation, and remaining useful life estimation.

– Equip NASA with the ability to monitor the health of onboard electronics of an aircraft in actual operating conditions to increase the safety and availability of the aircraft.



CALCE PHM Methodology

• Existing sensor data• Bus monitor data• BIT, IETM

SystemPrognosticsAnd Health Monitoring

Remaining Life Assessment

CALCE – ePrognosticsSensor System

Life Cycle Logistics and Cost Analysis

Virtual Life Estimation

Physics-of-Failure Based Approach

Data Driven Approach

Failure Modes, Mechanisms and Effects Analysis (FMMEA)

Maintenance Records

Failure Mechanisms

Failure Modes

Life Cycle Profile

Physics of Failure Models

Design Data

Detection, Severity & Occurrence

Fusion Prognostics



CALCE Fusion Prognostics (simplified)Healthy Baseline

Continuous Monitoring

Parameter Isolation

Alarm

Remaining Useful LifeEstimation

PoF Models

Anomaly?

Identify parameters

Data Driven Algorithms

FailureDefinition

Yes

No

Database and Standards



Package Interconnections

EMIsusceptibility

EMIgeneration Crosstalk

Circuitry

Excessivedelay time DC drop

Connections and Connectors

DI noise

Connector corrosion

PTH barrelfatigue

Lead padcorrosion

Tracecorrosion

Tracefracture

Laminateplasticization

Delamination

Tglimitation

Fiber resindebonding

CFF

Dendriticgrowth

Intermetallicformation

GullwingLow cycle fatigueHigh cycle fatigue

Shock fracture

BGALow cycle fatigueHigh cycle fatigue

InsertionPullout

Lead fatigueHigh cycle fatigue

Shock fracture

COBLow cycle fatigueHigh cycle fatigue

LCCLow cycle fatigueHigh cycle fatigue

Shock fracture

J-leadLow cycle

fatigueHigh cycle

fatigueShock fracture

Pressure contactSpring relaxation

Pin in socketPin fretting

Edge cardFinger fretting

Printed Wiring Board

Failure Sites and Mechanisms in Electronic Hardware



CALCE Probabilistic PoF Prognostics

,.....),,,( Dmean tdtdssfw s∆=∆Damage,

Time (t)

Load

(s

)

Embedded Data Reduction and Load Parameter Extraction

Remaining life = 1 - g(Σ∆w)

Mean load (Smean) Ramp rate (ds/dt)0

0.25

0.5

Range (∆s)

Freq

uenc

y

Dwell time (tD)0

0.25

0.5

0

0.25

0.5

0

0.25

0.5

0

10

20

30

40

50

60

70

80

90

100

7/19/0612:00 AM

7/20/0612:00 AM

7/21/0612:00 AM

7/22/0612:00 AM

7/23/0612:00 AM

7/24/0612:00 AM

7/25/0612:00 AM

7/26/0612:00 AM

7/27/0612:00 AM

7/28/0612:00 AM

Deg

rees

C



Physics of Failure Methods Work Well HerePhysics of Failure Methods Work Well HereData-Driven Methods Work Well HereThe monitored data may not directly relate to a specific

failure mechanism or to inputs to a failure model



Data Driven Analysis Procedure

Acquire new observations

Create “healthy”

profile matrix Calculate expectations

Calculate actual residuals

RX=Xexp-XobsSelect

parameters to

monitorAssess

variability wrt other healthy

data

Calculate expectations

Calculate healthy residuals

RL =Lexp-L

TrendAnalysis



CALCE Fusion Prognostics (simplified)Healthy Baseline

Continuous Monitoring

Parameter Isolation

Alarm

Remaining Useful LifeEstimation

PoF Models

Anomaly?

Identify parameters

Data Driven Algorithms

FailureDefinition

Yes

No

Database and Standards



Case Study #1: Circuit Card AssemblySubjected to Temperature Cycling



PoF Approach for Time to Failure Estimation

• A failure modes mechanisms and effects analysis (FMMEA) was carried out to determine the critical modes and mechanisms that affect the components

• Models for each identified critical failure mechanism were selected to calculate the time to failure from a database of PoF models.

• The mean cycles to failure for the first to fail component: 256 I/O BGA was calculated to be 1038 cycles

• The 3 sigma cycles to failure: 256 I/O BGA was calculated to be 720 cycles



Anomaly Detection

• A healthy baseline was established for each type of component using the first 100 thermal cycles of the test

• Estimates of the continuously monitored data were - assessed with a multi-variate state estimation

algorithm, - the residuals were statistically tested for

anomalies using SPRT, - the parameters causing an anomaly were

identified for assessment from the PoF database and the definition of failure from the PoF model



SPRT Signaled Alarms Starting at the 583rd

cycle



• The physics-of failure database analysis (from the data-driven analysis) provided a definition of failure for the resistance: 300Ω.

• Peak residual values from the time of anomaly detection was trended using regression analysis and the time taken for the resistance residuals to cross the 300Ω threshold was calculated.

• The estimates were updated every cycle• The time to failure, calculated based on the parameter trending

(resistance) was estimated to be 620 cycles. • This estimate was made at the 601st cycle (component failed at

the 631st cycle).

Data Driven Prognostics for RUL Estimation



RUL Estimates vs Product Failure

• Deterministic PoF mean cycles to failure estimate (t = 0): 1038 cycles (mean)

720 cycles (3 sigma)• Anomaly detected (possible intermittent failures)

583 cycles• The Fusion Prognostics cycles to failure estimate

(t = 601): 620 cycles• Actual failure 631 cycles



Comment on Prognostics-Based Qualification

and Screening



SPRT Signaled Alarms Starting at the 583rd

cycle


Center for Advanced Life Cycle Engineering 3030

• Fusion Prognostics look promising for prognostics

• PHM will be used for qualification, screening and continuous remaining life assessment.

• Prognostics will be embedded in most critical electronics within the next 10 years

Conclusions


Center for Advanced Life Cycle Engineering 31

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