prognostics and systems health management -...

Prognostics and

Systems Health Management

Michael PechtDirector and Chair Professor

CALCE Electronic Products and Systems CenterUniversity of Maryland

University of Maryland: 2012

• Started in 1856• About 48,000 students• Ranked 13th in the world in engineering programs,

by The Institute of Higher Education and Center for World-Class Universities

• Ranked 8th in engineering in the USA: Wall Street Journal

3 Copyright @ 2012PrognosticsTM

CALCE Overview

• The Center for Advanced Life Cycle Engineering (CALCE) formally started in 1984, as a NSF Center of Excellence in systems reliability.

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

• Supported by over 120 faculty, visiting scientists and research assistants

• Received NSF innovation award in 2009


CALCE Research Funding (over $6M): 2011• Alcatel-Lucent• Aero Contol Systes• Agilent Technologies• American Competitiveness Inst.• Amkor• Arbitron• Arcelik• ASC Capacitors• ASE• Astronautics• Atlantic Inertial Systems• AVI-Inc• Axsys Engineering• BAE Systems• Benchmark Electronics• Boeing• Branson Ultrasonics• Brooks Instruments• Buehler• Capricorn Pharma• Cascade Engineering • Celestical International• Channel One International• Cisco Systems, Inc.• Crane Aerospace & Electronics• Curtiss-Wright Corp• CDI• De Brauw Blackstone Westbroek• Dell Computer Corp.• DMEA• Dow Solar• DRS EW Network Systems, Inc.• EIT, Inc.• Embedded Computing & Power• EMCORE Corporation• EMC

• EADS - France• Emerson Advanced Design Ctr• Emerson Appliance Controls• Emerson Appliance Solutions• Emerson Network Power• Emerson Process Management• Engent, Inc.• Ericsson AB• Essex Corporation• Ethicon Endo-Surgery, Inc.• Exponent, Inc.• Fairchild Controls Corp.• Filtronic Comtek• GE Healthcare• General Dynamics, AIS & Land Sys.• General Motors• Guideline• Hamlin Electronics Europe• Hamilton Sundstrand• Harris Corp• Henkel Technologies• Honda• Honeywell• Howrey, LLP• Intel• Instituto Nokia de Technologia• Juniper Networks• Johnson and Johnson• Johns Hopkins University• Kimball Electronics• L-3 Communication Systems• LaBarge, Inc• Lansmont Corporation • Laird Technologies • LG, Korea• Liebert Power and Cooling• Lockheed Martin Aerospace• Lutron Electronics• Maxion Technologies, Inc.• Microsoft

• Motorola• Mobile Digital Systems, Inc.• NASA• National Oilwell Varco• NAVAIR• NetApp• nCode International• Nokia Siemens• Nortel Networks• Nordostschweizerische Kraftwerke

AG (NOK)• Northrop Grumman• NTSB• NXP Semiconductors• Ortho-Clinical Diagnostics• Park Advanced Product Dev. • Penn State University• PEO Integrated Warfare• Petra Solar • Philips• Philips Lighting• Pole Zero Corporation• Pressure Biosciences• Qualmark• Quanterion Solutions Inc• Quinby & Rundle Law• Raytheon Company• Rendell Sales Company• Research in Motion• Resin Designs LLC• RNT, Inc.• Roadtrack• Rolls Royce• Rockwell Automation• Rockwell Collins• Saab Avitronics• Samsung Mechtronics• Samsung Memory• S.C. Johnson Wax• Sandia National Labs

• SanDisk• Schlumberger• Schweitzer Engineering Labs • Selex-SAS• Sensors for Medicine and Science• SiliconExpert• Silicon Power• Space Systems Loral• SolarEdge Technologies• Starkey Laboratories, Inc• Sun Microsystems• Symbol Technologies, Inc• SymCom• Team Corp• Tech Film• Tekelec• Teradyne• The Bergquist Company• The M&T Company• The University of Michigan• Tin Technology Inc.• TÜBİTAK Space Technologies• U.K. Ministry of Defence• U.S. Air Force Research Lab• U.S. AMSAA• U.S. ARL• U.S. Naval Surface Warfare Center• U.S. Army Picatinney/UTRS• U.S. Army RDECOM/ARDEC• Vectron International, LLC• Vestas Wind System AS• Virginia Tech• Weil, Gotshal & Manges LLP• WesternGeco AS• Whirlpool Corporation• WiSpry, Inc.• Woodward Governor


CALCECenter for Advanced

Life Cycle Engineeringfor

Electronic Products and Systems

Research Contracts

• Large scale programs • Medium to long-term durations• Contractual agreements• Examples:

Software development,testing, training programs

Electronic Products

and SystemsConsortium

•Risk assessment, management, and mitigation for electronics

Prognostics and Health

ManagementConsortium

• Techniques based on data trending, physics-of-failure, and fusion

Education

• MS and PhD programs• International visitors• Web seminars • Short courses for

industry

LabServices

• Small to medium scale• Rapid response• Examples:

Failure analysis, measurement,design review, supplier assessment

Standards• Putting CALCE

research to work for industry

• Examples:IEEE GEIA IPCJEDECIEC


CALCE Test Services and Failure Analysis Laboratory

• Conducted over 130 failure analysis studies for over 100 companies already in 2012

• Review of Electrical and Mechanical Design• Review of failure mode, mechanisms and effect analysis

(FMMEA)• Identification of critical-to-quality parameters• Materials and parts characteristics RoHS compliance evaluation • Virtual qualification and reliability assessment• Co-operation with government-industry data exchange program • Provides alerts for two fortune 500 electronic companies.


Non-Destructive EvaluationPhoenix Nanomex 3D X-ray Imaging SystemScanning Acoustic Microscope (SAM)Fourier Transform Infrared Spectroscopy (FTIR)Scanning Electron Microscope (SEM)Energy Dispersive Spectroscopy (EDS)Atomic Force Microscope (AFM)Automated Contact Resistance Probe (ACRP)Infrared Microscopy

Microelectronic Devices AnalysisAutomated Test Equipment

• IC Functional Parametric Testing• Temperature Range -80oC to 225oC

Impedance Analyzer (1.86Gz)Microcircuit ProbeTransistor and IC Curve TracerHigh Power Curve TracerDynamic Signal AnalyzerEvent DetectorsElectrometerLCZ Meter

Thermal Assessment and Management Hughes Infrared ProbeyeLiquid Crystal ThermographyLow Speed Wind Tunnel Hot Wire AnemometerFlow Visualization SystemHigh Speed Video CameraLaser Flash Thermal Properties MeasurementMaterials Thermal Emissivity Measurement Flow/Velocity Measurement FacilitiesPressure Measurement FacilitiesData Acquisition System

Materials Characterization Differential Scanning Calorimeters (DSC)Micro-Mechanical Materials Tester Thermo-Mechanical Analyzer (TMA)Dynamic Mechanical Analyzer (DMA)Creep Testing EquipmentThin Film Analyzer (TFA)Moisture Absorption SystemMaterials Test Systems (5 grams to 200 kg)

• High-strain rate characterization (100/sec)• Tests can be conducted in vacuum, inert or

reactive atmospheres (-125oC to 300oC)*Micro-Hardness Tester*Micro-Fatigue Tester*Adhesion Tester

Opto-Mechanics ExperimentationGeometric MoireMoire InterferometryMicroscopic Moire InterferometryShadow MoireInfrared Fizeau InterferometryTwyman/Green Interferometry

Sample Preparation Diamond SawDiamond BladePolishing and Grinding StationPlasma Etching

Failure AnalysisStereoscope (10x-63x)Optical Microscope (25x-2,500,000x)Image AnalysisFocused Ion Beam (FIB) Scanning Probe Microscope (SPM)

• Wavelength Dispersive SpectroscopyFEI Environmental Scanning Electron Microscope (ESEM)

• (25x-600000x) • Energy Dispersive Spectroscopy (EDS)• In-situ Heating/Mechanical Testing• Oxford Energy Dispersive Xray system (EDX)

TA Instruments DMATransmission Electron Microscope (TEM)Wire Pull, Bond Shear, and Die Strength Tester Package DecapsulatorIon ChromatographSIR TestingHollow Fiber AssessmentReal-time Solder Reflow SimulationComponent Popcorning Assessment

Virtual Qualification LabcalcePWACADMP-IIcalceFASTDefects

WebbookPEMs Webbook

Accelerated Test WebbookPWA Assembly WebbookIntegral Passives WebbookPWA Failure Mechanism

WebbookSensor Technology Webbook

Environmental/Accelerated TestingTemperature-Humidity ChambersHALT Temperature-Vibration ChambersThermal Shock ChambersHAST Temperature-Humidity ChambersHigh Altitude Simulation Chamber

• Pressure, Humidity, and Temp. CyclingHigh Temperature Aging ChambersMixed Flowing Gas (MFG) ChamberElectrodynamic Vibration ChamberImpact and Drop Test Apparatus

CALCE Facilities and Capabilities

EMI/EMC Characterization Precision Spherical DipoleLow- and High Impedance Probes for EMI/EMC DiagnosticsShielding Effectiveness

Measurements

*EMC Analyzer*Wave GeneratorSemi-anechoic ChamberVector Network AnalyzerSusceptibility Testing


CALCE Reliability Capability Audit Program

• CALCE EPSC has developed a process for reliability capability evaluation.

• This service is available to:– companies assessing potential suppliers or contract

manufacturers – organizations seeking a competitive edge through

self-improvement and independent evaluation• Many audits (e.g., Nortel, Nokia, Emerson, Huawei, Astec)

have already been completed.


CALCE Product Development Benchmarking

• CALCE benchmarking services involves– determining the current state-of-practice of the company– assessing the strengths and weaknesses of the company – comparing company to best-in-class– providing process improvement suggestions

• CALCE has developed methods for benchmarking of electronic industry supply chain members including system integrators, contract manufacturers, electronic packaging assembler, and electronic test laboratories

• Example benchmarks– AlliedSignal– Lucas Aerospace (TRW)– Microsoft– Nortel Networks– Bay Systems– Huawei - China– Honeywell– Nokia– Emerson


CALCE Design Reviews

• CALCE EPSC performs design reviews on electronicproduct designs and prototypes, drawing on the facultyexpertise, characterization facilities, and virtual qualificationcapabilities at the Center.

• Design reviews provide an understanding of reliability risksin a product before their release to the field.

• To date, CALCE has performed over 300 design reviews forleading electronics manufacturers.

• If you are interested, email [email protected] [email protected].


CALCE Publications


CALCE EPSC Short Courses for IndustryCourses offered:• Physics of Failure and Reliability • Plastic Encapsulated Microelectronics • Pb-free: Guide to Reliable Product Development• High Temperature Electronics • Optical Methods as a Tool for Microelectronics Product Development • Electronic Product and System Cost Analysis • Excellence in Supply Chain Management • Parts Selection and Management • The Next Generation of Electronic Parts: Processes, Tests, Applications, and Risks • Electronic Parts Outside the Manufacturer-specified Temperature Range • Electronic Part Obsolescence Forecasting, Mitigation and Management • Design for Reliability: Performing and Initial Assessment • Accelerated Product Qualification • Thermal Design and Assessment of Electronic Systems • Methodology and Tools to Solve EMI/EMC Problems • Failure (Root-Cause) Analysis of Electronic Products


CALCE: Monthly Continuing Educational Unit Webinars

•Power Electronics for Sustainable Energy Systems•Interconnect Durability of Package on Package Assemblies•Throwaway Electronics•Early Detection of Solder Joint Degradation,•Material Characterization Techniques for Part Authentication•Light Emitting Diode Failure Mechanisms•Impact of Isothermal Aging on Interconnect Reliability of Select Lead-Free Solders•Electrolytic Capacitor Quality and Reliability•Mitigation Strategies for Tin Whiskers


CALCE MissionProvide a knowledge and resource base to support the development and

sustainment of competitive electronic products

Strategies for Risk Assessment, Mitigation

and Management

Life Cycle Risk, Cost Analysis and Management

Accelerated Testing, Screening

and Quality Assurance

Supply Chain Assessment and Management

Physics of Failure, Failure Mechanisms

and Material BehaviorDesign for Reliability and

Virtual Qualification

Prognostics and Health

Management


Since 2002, Field Failures of These Devices Amounted to over $10B in Losses

Manufacturer IC function Package type Final product

Motorola Frequency Synthesizer, etc

Unspecified Automotive anti lock brake system (ABS)

Philips Unspecified 80 pin QFP Quantum, Hard disk drive

Cirrus Logic HDD controller 208 pin QFP Fujitsu, Hard disk drive

Infineon SIPMOS Small‐Signal‐Transistor

4 pin SOT 223 Unspecified

FairchildSemiconductor

Low Voltage Buffer Liner Driver

48 pin TSSOP Seagate

N‐channel MOSFET Various TSSOPs HP

Maxim Unspecified 48 pin TQFP Sony

Intersil Corp LSI’s for WLAN 20 pin QFN Unspecified

Conexant Unspecified ETQFP Unspecified

LSA Unspecified 128 pin TQFP Unspecified


Intel’s Flawed Chip2011

• Intel stopped shipping a new chipset after discovering a design flaw that causes computer connection ports to fail in three to five years, blocking access to stored data

• The standard tests of the chipset by Intel uncovered no problems.

• The interruption in production reduced Intel’s first-quarter 2011 revenue by $300 million.

• $700 million more was spent on fixing the chipsets and replacing those already built into computers.


Example: Nvidia Chipset Problem

• Around 2006, customers of various computers started to complain of intermittent failures in graphic processing units and communication processors

• Computer companies could not identify the problem since field returns generally exhibited no-fault found (NFF) behavior

• In 2008, Nvidia “confirmed” that certain computers were failing at higher than normal rates

• HP issued an extended warranty (and changed the BIOS) on many of its computers. Other companies followed. Loses were over $1B


Computer Failures : 2009 StudyA 2009 analysis of 30,000 new laptops found that in the first three years of ownership, nearly a third of laptops will fail.


September 2010Over 40,000 Toshiba Computers Recalled

- Took more than a year to track down, highly intermittent -


Failure of GovernmentFlight Routing Computer

November 18, 2009

• A computer failure in the FAA’s system for routing flights caused hundreds of flights to be delayed in cities across the USA

• This was the third failure of this system in two years


Millennium Bridge Shutdown due to Electronic Circuit Board Failure

World’s first bridge built to tilt to let ships pass under it, was shutdown due to a failure of the control computer. The bridge construction was valued to be $42 million.


Washington DC Rail Crash: 2010


Train Collision in Wenzhou, China, July 23, 2011


Airline Cancels 152 FlightsMarch 26, 2011

• Alaska Airlines and its Horizon Air affiliate canceled 18 percent of the airlines combined schedule.

• The reason was failure of the computer used for flight planning


Oct 2010: Nissan 2.1 Million Car RecallFaulty Ignition Computer


Honda Recalls

• In August 2011, Honda recalled 2.3 million also due to a computer system reliability problem

• In September 2011, Honda recalled another 1 million cars over computer glitches


NASA Rocket Launch Failure: March 2011

• The Taurus XL rocket carrying NASA's Glory satellite lifted off but fell to the sea several minutes later.

• The Taurus rocket has launched nine times, six of them successful.

• The $424 million mission is managed by the NASA's Goddard Space Flight Center and launched by Orbital Sciences Corporation


Failure Categories in 2004Other Sites 7%

Connectors 3%Interconnects/Solder Joints 13%

PEM 21%

Printed Board 26%

Capacitors 30%

Failure sites identified by root-cause analysis of 170 field returns of various electronic products from over 70 different companies (CALCE internal document)

Active Parts 21%

CALCE Failure Analysis Categories

Whyare mature part types failing ?


Reliability Challenges

• Why didn’t the qualification tests uncover the problems in advance ?

• Is it possible that components can pass the qualification tests but not be ‘healthy” ?

• Do we miss detecting failures that are occurring, but in an intermittent manner ?


The Electronics Marketplace

• Electronic materials, devices, assemblies, and products are changing very rapidly

• Supply-chains have become extremely complex and tremendous price pressures exist

• Severe competition from China


For Military SystemsSustainment costs - which are related directly to

reliability - dominate total system costsRDT&E Procurement Ops & Sustainment

Fixed Wing Fighters 9% 30% 62% Rotary Wing 6% 29% 64% Ground Systems 4% 24% 73% Surface Ships 1% 31% 68%

• Sustainment costs have five to ten times more impact on total life cycle costs than do RDT&E costs.

• US Gov’t studies indicate at least a seven-fold payback for up-front investment in better reliability.


• 1965: US military developed Mil-Hdbk-217: a handbook for reliability prediction of electronics

• 1980s: The telecommunications industry adopted the 217 approach with various modifications

History Reliability Prediction of Electronics


Mil-Hdbk-217 Methodology

= Q (C1 T V E) L


Electronics Reliability Prediction History of US Military

Mil-Hdbk-217A Dec 1965Preparing Activity: Navy

Single point constant failure rate of 0.4 failures/million hours for all monolithic ICs

Mil-Hdbk-217B July 1973Preparing Activity: Air Force Rome Labs

RCA/Boeing models simplified by Air Force to follow statistical exponential distribution

Mil-Hdbk-217C April 1979Preparing Activity: Air Force Rome Labs

Band-aid for memory. For example when 4k RAM model was extrapolated to 64K, predicted MTBF = 13 sec

Mil-Hdbk-217D Jan 1982Preparing Activity: Air Force Rome Labs

Band-aid. No technical change in format

Mil-Hdbk-217E Oct 1987Preparing Activity: Air Force Rome Labs

Band-aid. No technical change in format

Mil-Hdbk-217F Dec 1990Preparing Activity: Air Force Rome Labs

CALCE, University of Maryland – Change in direction recommended.


• 1988: CALCE was awarded a 3 year $2M US government contract to assess Mil-Hdbk-217 and provide guidance for the future

• Conclusion: Mil-Hdbk-217 and progeny (Telecordia, Prism and other similar handbook methods ) have fundamental flaws built into them.



Handbooks Start to Become Cancelled

• British Telecom, “Handbook of Reliability Data for Components Used in Telecommunications Systems,” (1993). {CANCELLED}

• Italtel, “Italtel Reliability Prediction Handbook,” (1993). {CANCELLED}

• Nippon Telegraph and Telephone Corporation, Standard Reliability Table for Semiconductor Devices, (1986). {CANCELLED}

• Siemens Standard SN 29500, “Reliability and Quality Specification Failure Rates of Components,” (1999). {CANCELLED}

• SAE Reliability Prediction Software PREL (1990). {CANCELLED}


600-100 0 100 200 300 400 500

Bellcore (currentlyTelcordia)CNETHRDMil-Hdbk-217Siemens

Board one

Board two

Board three

Board four

Board five

Board six

% Deviation from Field Failure Rate

Comparison of Various Handbook Prediction Methodologies

Jones, J. and Hayes, J., “A Comparison of Electronic Reliability Prediction Models,” IEEE Transactions on Reliability, Vol. 48, No. 2, pp. 127-134, 1999.


Comparison of Failure Prediction to Field Results - U.S. Army SINCGARS Radio Study -

• MTBF requirement is 1250 hours with 80% statistical confidence• Vendors included: Ericsson, Harris, Marconi, Racal, Rockwell, Sel, Tadrian,

Thompson and TransworldCushing, Michael J., et al. “Comparison of Electronics-Reliability Assessment Approaches,”

IEEE Transactions on Reliability,Vol. 42, No. 4, pp. 540-546, December 1993.

Vendor Mil-Hdbk-217 MTBF (Hours) Observed MTBF (Hours)A 7247 1160B 5765 74C 3500 624D 2500 2174E 2500 51F 2000 1056G 1600 3612H 1400 98I 1250 472


U. S. Military View of Mil-Hdbk-217

“… Mil-Hdbk-217, Reliability Prediction of Electronic Equipment, and progeny, is not to be used as it has been shown to be unreliable and its use can lead to erroneous and misleading reliability predictions.”

October 1994

Decker, Assistant Secretary of the Army (Research, Development, and Acquisition), Memorandum for Commander, U.S. Army Material Command, Program Executive Officers, and Program Managers


“... GM concurs and will comply with the findings and policy revisions of Feb. 15, 1996 by the Assistant Secretary of the U.S. Army for Research, Development and Acquisition. … Therefore: Mil-Hdbk 217, or a similar component reliability assessment method such as SAE PREL, SHALL NOT BE USED.”

General Motors Reliability Policy

GM North American Operation,Technical Specification Number: 10288874, June 4, 1996.


• 1999: CALCE awarded 5 year $12M US contact to develop physics of failure models (software) for electronics industry to replace 217-based methods

• US Army – AMSAA established a physics-of-failure laboratory



2003: IEEE 1413 and IEEE 1413.1


• Shows that there is no value in the use of Mil-Hdbk-217, 217-Plus, Telecordia, FIDES, and progeny prediction methods

• Physics-of-failure methods are necessary for good reliability assessment and prediction

IEEE 1413 and 1413.1


CALCE Electronics PoF SoftwareSystem-Level Analysis

Composite Two-Wheeled Trailer VibrationTable 514.5C-VII, Fig. 514.5C-2

0.0001

0.0010

0.0100

0.1000

1.0000

1 10 100 1000

Frequency (Hz)

PSD

(g2 /H

z)

Board-Level AnalysisMechanical Loads Thermal Loads

Board Displacement Thermal Overstress Analysis

Component Life Estimates


Comanche• Commonality w/ AF F-22• Commercial ICs Inserted• $50M O&S savings

Bradley Fire Support Vehicle• Identified potential problems• Increased Reliability

Life Cycle PoF Analysis Provides Considerable ROI

Applications Using CALCE SoftwareSeagate •Virtual Qualification

Emerson•Virtual Qualification of CCA•Failure assessment of a individual component.

Mars Path FinderVerified robustness of flight CCA

AAAVVirtual Qualificationof circuit cards

VISTA ControlsConducted virtualqualification of militaryCCA

CPU CardMezzanine Card

Assembly

JSTARS Ground Station• PoF Analysis on circuit cards• Recommended commercial

processor circuit card• $12M Savings

GM83% reduction in design issues>10% reduction in time to markert

HoneywellVirtual qualification of enginecontroller


JEDEC Standards Focused on Physics of Failure

• JESD34, Failure-Mechanism-Driven Reliability Qualification of Silicon Devices

• JESD47, Stress-Test Driven Qualification of Integrated Circuits

• JESD94, Application Specific Qualification Using Knowledge Based Test Methodology

• JEP148, Reliability Qualification of Semiconductor Devices Based on Physics of Failure and Risk and Opportunity Assessment


• Shows that there is no value in the use of Mil-Hdbk-217, 217-Plus, Telecordia, FIDES, and progeny prediction methods

• Physics-of-failure methods are necessary for good reliability assessment and prediction, but one also needs a good assessment of the “conditions of use”

IEEE 1413 and 1413.1


What are the Use Conditions of Products


Example: Apple Computer Study - A Transportation Load Monitoring Study -

• A computer monitor was shipped from College Park, Maryland to San Pedro, California and back via Federal Express, and a sensor suite was attached to the monitor to record shock (in all three axes), temperature and humidity.

• The sensor suite was programmed to:– record shock data only if the magnitude of the applied shock was greater

than 2.0 g or less than –2.0 g,– record shock data at a frequency of 1500 samples per second, and– record temperature and humidity data every 10 minutes.

• The unit was packaged and shipped via Federal Express to California on October 11, 2000 at 6:59 PM (EST).


Vibration and Shock Loads

1.0

2.0

3.0

4.0

Peak

G lo

ad (m

/s2 )

19.412.55

32.43

2.22.3

17.5316.93

3.15

17.11

10.91

38.44

9.5

Received in California12:45 PM, October 12

Sent from California6:24 PM, October 14

Received in Maryland12:44 PM, October 16

Sent from Maryland6:59 PM, October 11

g RM

S(m

/s2 )

0.0

Trip time (days)

1.0

2.0

3.0

4.0

Peak

G lo

ad (m

/s2 )

19.412.55

32.43

2.22.3

17.5316.93

3.15

17.11

10.91

38.44

9.5

Received in California12:45 PM, October 12

Sent from California6:24 PM, October 14

Received in Maryland12:44 PM, October 16

Sent from Maryland6:59 PM, October 11

g RM

S(m

/s2 )

0.0

Trip time (days)


TypicalUse conditions

Use category Tmin(°C)

Tmax(°C)

ΔT (°C) Hours Yearly

cyclesConsumer 20 55 35 12 365Computer 25 45 20 2 1460Telecom 10 45 35 12 365Commercial aircraft 20 40 20 12 365Industrial/ auto 30 50 20 12 185Military ground/ship 5 45 40 12 100Space 20 55 35 1 8760Military avionics 0 80 80 2 365Auto (under hood) 20 80 60 1 1000

IPC-SM-785, Guidelines for Accelerated Reliability Testing of Surface Mount Solder Attachments, November 1992.

Beware of “Standard” Profiles Example: IPC-785


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.


Monitoring Notebook Usage Profile


Recent Study by CALCE with Major Computer

Manufacturer

- Impact on Qualification -


Sensor Module


Sensor Card Users

Ireland: 19

USA: 79

Malaysia: 14

Japan: 7

China: 23


• Data Storage• Summary• Reports

Database

Data Process Flow

Server withDaily uploads

Data Analysis• Data Processing• Statistics• Analysis

Sensor Module


Data Analysis

Data file

Operating condition

Non-operating condition

Temperature (T)

Rel. Humidity (RH)

Temperature/Rel. Humidity combination

Outputs: •No. of events•Daily mean•Daily std dev•Daily max•Daily min•No. of events over Spec •T at max RH, RH at max T•Grms of each events

Charts: •Temp histogram•RH histogram•TH usage profile charts•Acceleration histogram•Grms histogram

Same as Operating

Condition above

Statistical AnalysisData Processing

Vibration (Vib)


Example Temperature Data

0

10

20

30

40

50

60

70

80 1

0/16

/03

10/

20/0

3

10/

21/0

3

10/

22/0

3

10/

22/0

3

11/

01/0

3

11/

01/0

3

11/

02/0

3

11/

03/0

3

11/

04/0

3

11/

04/0

3

11/

04/0

3

11/

04/0

3

11/

04/0

3

11/

05/0

3

11/

05/0

3

11/

05/0

3

11/8

/03

11/

11/0

3

11/

11/0

3

11/

11/0

3

11/2

3/03

11/2

3/03

11/2

3/03

11/2

3/03

11/2

3/03

11/2

4/03

11/2

4/03

11/2

4/03

11/2

5/03

11/2

5/03

12/2

/03

12/2

/03

12/2

2/03

12/2

2/03

12/2

3/03

12/2

3/03

12/2

3/03

12/2

3/03

Tem

pera

ture

(C)

HeatsinkHDD

Atmospheric Ambient

Date


Distribution of Temperatures Experienced by Heat-sink and HDD

0.00

0.05

0.10

0.15

0.20

0.25

0-5 6-1011-1516-2021-2526-3031-3536-4041-4546-5051-5556-6061-6566-7071-75

Absolute Temperature Range (Celcius)

Per

cent

Fra

ctio

n of

Tot

al

Heatsink

HDD


Analysis of Thermal Range Time- temperature history is converted into equivalent cycles

0

5

10

15

20

25

30

35

40

45

50

Delta T (Celcius)

Num

ber o

f Occ

uren

ces

Heatsink

HDD

937 739


Results

Parameters Current Spec. Observed extreme Values

No. of observations over Spec. per day per user (mean/95%)

Operating Vibration 0.6 GRMS 3.16 GRMS 2.8/7.8

Non-operating/Storage

Vibration 1.3 GRMS 5.65 GRMS 0.2/1.0

Temperature Range –40 to 65 C 10.5 to 69.8 C0.2/0.0

R.H. Range 5% to 95% 0.53% to 98.9%

Temp at Max. Humidity 39C @ 95% RH 28.4C @ 98.9%0.6/0.0

Humidity at Max. Temp 20% RH @ 65C 2.5%RH @ 69.8C


Qualification Tests

Time

50

100

1000 2000 3000 4000

*

•

Test Condition

Application Conditions - of - Use

Profile


Example: LED Lighting Systems

• In 2008, the Government of China Xiamen city started installing high power LEDs for street lighting.

• The manufacturer promised a system life time of 100,000 hours (over 10 years of reliable operation),

• The city government discovered the lights failed within 3 months after installation.


Health and Usage Monitoring


Health is the extent of deviation or degradation from an expected normal condition.

What should we monitorand

how do we monitor?


Health Monitoring of Batteries

79


Health Monitoring of BatteriesIdentifying weak or failing battery cells in service

Monitored Parameter

Invasive Technical Value Technical Weakness

Temperature differential

No Shows high battery temperature

Need to combine temp. with other data to find fault

Float current No Indicates high resistance battery current path

Requires each parallel string to be monitored individually

Battery conductance

No Passively finds weak cells/batteries

No need for battery discharge to indicate relative state of health

High/low battery temperature

No Can signal thermal stress problem

Location of sensors is critical and ambient temp. variation needs to be considered

High/low string voltage

No May indicate rectifier problem Indicates state of charge cannot predict capacity


Health Monitors for Hard Disk Drives


Health Monitors for Hard Disk Drives

• Head flying height: A downward trend in flying height will often precede a head crash.

• Error Checking and Correction (ECC) use and error counts: The number of errors encountered by the drive, even if corrected internally, often signals problems developing with the drive.

• Spin-up time: Changes in spin-up time can reflect problems with the spindle motor.

• Temperature: Increases in drive temperature often signal spindle motor problems.

• Data throughput: Reduction in the transfer rate of data can signal various internal problems.

• Heads/head assembly– crack on head– head contamination or resonance– bad connection to electronics

module• Motors/bearings

– motor failure– worn bearing– excessive run out– no spin

• Electronic module– circuit/chip failure– Interconnection/solder joint failure– bad connection to drive or bus

• Media– scratch/defects – retries– bad servo– ECC corrections

Parameters MonitoredReliability Health Issues


Health Monitoring Approach For Anomaly Detection

Acquire new observations (X)

Create “healthy”

profile matrixCalculate

expectations (Lexp)

Calculate actual residuals

RX=Xexp-XobsSelect

parameters to

monitorAssess

variability w.r.t. other healthy

data (L)

Calculate expectations

(Xexp)

Calculate healthy residuals

RL =Lexp-L

Anomaly warning

University of MarylandCopyright © 2012 CALCE

85Center for Advanced Life Cycle Engineering

Mahalanobis Based Anomaly Detection

Training Data (ICE, VCE, T)

Normalize Data

Calculate Correlation Matrix

Calculate Mean and Std Dev.

timeMD

val

ue

Test Data

Test Data Normalization

Test MD Values

timeMD

val

ueDecision

Healthy System Test System

T

iZ1CiZp1MD


Ericsson- CALCE PHM Implementation

020000400006000080000

100000120000140000160000

0 2 4 6 8 10 12 14

Time (hour)

Freq

uenc

y (H

Z)

Anomaly detection

05.0 05.0

Hours x 1000


EADS HUMS and CALCE HUMSMicrocontrollerTemperature & RH sensor Terminals for data

communication

FRAM memoryTerminals for external sensors

Terminals for battery

2-axis accelerometer

RFID

Courtesy of EADS


Automotive Health (Warranty) Monitoring

Back up collision, parking

proximity detection

YAW and acceleration sensors for airbag deployment

Tire pressure monitoring

Power train Control Module

Throttle position monitoring and control

Ignition and engine control monitoring

Oil/fuel pressure and flow monitoring

Rollover detection

Collision avoidance, night vision, and front crash detection

Side crash detection

Steering angle sensor and stability control

Temperature, humidity sensor and comfort control

Charge sensor (battery)

Angular acceleration (suspension)

Rain sensor and wiper control

Emissions sensor


Prognostics

Techniques utilized to determine the remaining useful life with a defined level of confidence for a specified coverage of anticipated events


Remaining Life Assessment

,.....),,,( Dmeant

dtdssfw sDamage,

Time (t)

Load

(s)

Embedded Data Reduction and Load Parameter Extraction

Remaining life = f ( 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

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7/25/0612:00 AM

7/26/0612:00 AM

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Deg

rees

C


Failure Sites and Mechanisms in Microelectronic Devices

DieMetallization

Die Device

ConstraintCavitation

Corrosion Electro migration

HillockFormation

Slow Trapping

ElectricalOverstress

TDDB ESD

IonicContamination

Second Breakdown

Die Fracture

DieFatigue

Bond PadFatigue

Surface ChargeSpreading


Interconnects Case Leads AttachSubstrate

WirebondsFlip-Chip

Solder JointFatigue

PlasticHermetic

Delamination and Cracking

Solder JointFatigue

Intermetallicformation

SubstrateFracture

SubstrateFatigue

AdhesiveFracture

AdhesiveFatigue

Loss ofLid Seal

Failure Sites and Mechanisms in Electronic Components

Wire ShearFatigue

IntermetallicFormation

Wire FlexureFatigue

Loss ofLead Seal

CaseFracture

CaseFracture


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 fatigue

High cycle fatigueShock 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


Physics of Failure DatabaseFailure Mechanism Failure Sites Relevant Stress

Parameters Sample Model

FatigueDie attach,

Wirebond/TAB, Solder leads, Bond

pads,Traces, Vias/PTHs,

Interfaces

Cyclic Deformations (D T, D H, D V)

Nonlinear PowerLaw (Coffin-

Manson)

Corrosion Metallizations M, DV, T, chemical Eyring (Howard)

Electromigration Metallizations T, J Eyring (Black)

Conductive Filament Formation

Between Metallizations M, ΛV Power Law (Rudra)

Stress DrivenDiffusion Voiding

Metal Traces s, T Eyring (Okabayashi)

Time Dependent Dielectric Breakdown

Dielectric layers V, T Fowler-Nordheim


In-situ Monitoring for Root Cause Analysis and Forecasting Maintenance

Monitored environmental and

operating conditions of test board

Simplified data (e.g., data

reduction, and cycle counting)

Performed physics-of-failure based

stress and damage assessment

Obtained the remaining life


Health Monitoring Provides Data for Probabilistic Physics of Failure Prognostics

,.....),,,( Dmeant

dtdssfw sDamage,

Time (t)

Loa

d (s

)

Embedded Data Reduction and Load Parameter Extraction

Remaining life = 1 - g(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 A M

7/22/0612:00 A M

7/23/0612:00 AM

7/24/0612:00 AM

7/25/0612:00 AM

7/26/0612:00 AM

7/27/0612:00 A M

7/28/0612:00 A M

Deg

rees

C


In-Situ Monitoring for Remaining Life Prediction and Forecasting Maintenance

0 5 10 15 20 25 30 35 40 45 50Time in Use (days)

0

10

20

30

40

50

Est

imat

ed R

emai

ning

Life

(day

s)

Actual life from resistance monitoring = 39 days

Day of Car Accident

Estimated life after accident (LCM = 40 days)

Estimated life after 5 days of data collection = 46 days)


NASA Space Shuttle Remote Manipulator System

97

The first SRMS flew on the space shuttle in November 1981.

By using the existing sensor data, along with inspection and physics-of-failure

software analysis, it was found that there was little degradation in the

electronics and they could be expected to last another 20 years.


Case Study: NASA Solid Rocket Booster Electronics

• The space shuttle solid rocket booster (SRB) was designed for a 20 year life.

• The objective of the study was to assess the possibility of extending the use of the electronics beyond the designed life.


Life Cycle Profile: SRB Flight Segments

Event 1

Events 2 to 14

Events 15 and 16 Events

17 to 29

Events 30 and 31

Event 32


Vibration History from One Flight


Remaining Life Assessment of SRB Electronic Hardware

• It was predicted that the electronics would survive another 40 missions.

• HOWEVER, analysis found that the mechanical brackets would not survive additional missions due to damage accumulation from prior missions.

• Awarded best article in IEST 2007: “Virtual Remaining Life Assessment of Electronic Hardware Subjected to Shock and Random Vibration Life Cycle Loads,”


2003: US Military Requires

Prognostics to be Included in All New Weapon Systems


Prognostics Health Monitoring Enabled Logistics Decisions for Aircraft Carrier

Decisions based on PHM Assessments• Assess level of aircraft maintenance• Prioritize maintenance jobs• Update launch schedule• Manage deck effectively

Wireless transfer of mission usage data

University of MarylandCopyright © 2012PHM Group 104

Prognostics and Systems Health Management Approach

Detection

(1) Remote Monitoring(1) Remote Monitoring

Raw Sensor DataRaw Sensor Data

(2) Data Pre-Processing

(2) Data Pre-Processing

Time-stamped FeaturesEvent MessagesParametric Data

Time-stamped FeaturesEvent MessagesParametric Data

(3) Anomaly Detection

(3) Anomaly Detection

Warnings & AlertsChange detection time

(4) Anomaly Identification(4) Anomaly

Identification

Anomaly Source ID:- System- Sensor

- Operator- Control

- Reference- Model

AssetAsset OperatorOperator Health Assessment

(6) Prognostics(6) Prognostics

Remaining Useful Life (RUL)

Prediction

(5) Diagnostics(5) Diagnostics

SubsystemFailure Modes

Subsystem HealthAssessment

(η, Deterior. Index)

ClassificationClassification

(7) Fault Accommodation

(7) Fault Accommodation

(8) Logistics Decisions

(8) Logistics Decisions

Corrective Action Identification

Corrective Action Identification

Part Level Health RUL AssessmentPart Level Health RUL Assessment

On-board Tactical Control

Inventory ControlInventory Control

MaintenanceActions

MaintenanceActions

OperationalActions

OperationalActions

Off-board Management

Source Management

Source Management

Customer/MaintainerCustomer/Maintainer

(9) Planning and Mgmt

(9) Planning and Mgmt

Plan

Mission Objectives & Requirements

Mission Objectives & Requirements

Value Optimization

Value Optimization

Maintenance Culture

Maintenance Culture

Contract Engineering

Contract Engineering

Standards/Best

Practices

Standards/Best

Practices

Value Assessment

Value Assessment

ROI Forecast

ROI Forecast

Operational, Cost and Readiness Impact

Operational, Cost and Readiness Impact

Data LogisticsData Logistics

(c) General Electric Company - 2010

The business of PHM …

Sensors & Analytics provide intelligence and differentiated business processes that allow GE todevelop new products & services … and generate value over the life of the asset

105

High‐Value Assets

Sensors & Data Collection

Signal Processing and Analytics

Prevent catastrophicfailures & forced outages

Part Life Management

Performance Optimization

Better designs & retrofitsOperational Risk Mgmt

Intelligent Business Processes, New Products & Services

(c) General Electric Company - 2010

New Roles for PHM &Asset Management

New Business Models, Products• Extended warranties• Long term service agreements• Operation & Maintenance Agreements• New types of assets .. E.g. Renewable energy

Technology & Reliability, Risk• Costs of collecting, storing and analyzing datacontinues to fall .. moving from terabyte topetabyte level databases

• Reliability & Risk Management needs to operatein a real‐time 24/7 environment

106

How to Make Money with Diagnostics/ Prognostics (IVHM)?

• Old Paradigm– Sell Vehicles (Aircraft)– Sell Spares– Operator Owns Vehicle Fleet, Maintains Vehicles and Provides Crew

• New Paradigm– Sell Guaranteed Service

• Gate Dispatch Reliability• Seat Miles • Power by the Hour (Rolls-Royce)

– Service Provider May Own Fleet, Maintains Fleet– Operator Provides Crew

Boeing Phantom Works Support and Services - IVHM

Copyright © 2006 Boeing. All rights reserved. 107

University of Maryland

• Provide an early warning of failures• Provide guidance to extend warranties• Optimize qualification tests• Provide efficient fault detection (CND)• Forecast maintenance as needed • Improve designs

PHM Objectives


The FutureHealth management and prognostics will be an

integral part of all products and systems

prognostics and systems health management -...

Documents