Toward Reliable Engineered System Designhome.engineering.iastate.edu/~jdm/wesep594/WESEP...

System Reliability &Safety LaboratorySRSL 1

Chao Hu

Assistant Professor

Department of Mechanical Engineering

Iowa State University

Toward Reliable Engineered System Design:RELIABILITY-BASED DESIGN AND PROGNOSTICS AND HEALTH MANAGEMENT (PHM)


Motivation

I-35W Bridge collapse due to faulty design, Aug. 2007

Consequence: 13 deaths, 145 injured, $60 million loss

Wind turbine collapse due to faulty maintenance, Feb. 2008

Consequence: Collapse of whole wind turbine

Boeing 787 Dreamliner fire due to overheated Li-ion battery

Japan Airlines (JAL) in Boston, Jan. 2013

Consequence: Over $1.1 million daily loss due to groundings


Power transformer fire due to faulty bushing, Jul. 2002

Consequence: $5 million property & business loss

UPS flight fire possibly due to overheated Li-ion battery, Feb. 2006

Consequence: 3 injured, loss of whole airplane

Research Questions:

Q1. Is it possible to design a system with near-zero failure probability?

Q2. Is it possible to anticipate and prevent failures during system operation?

Motivation


Reliability-

Based Design

Fatigue Life

PD

F

Optimized

Initial

Limit

Scientist @ MDT / Faculty @ ISU

Research Timeline

PhD Student @ UMD

2007 2009 2011 2013

Prognostics and Health Management (PHM)

Sensing Reasoning Prognostics

Remaining Useful Life (RUL)

PD

F

Health Index 1

He

alt

h I

nd

ex

2

Battery Prognostics

Deep Brain StimulatorsLi-Ion Rechargeable

2015


Scientist @ MDT / Faculty @ ISU

Research Timeline

PhD Student @ UMD

2007 2009 2011 2013

Photo credit

http://www.mpoweruk.com

Metal

particle

(−) Electrode

Separator

(+) Electrode

Inside a Lithium-Ion

Battery

2015


Design of Control Arm (US Army): Methodology

0

[Life at 1st hotspot=Limit]

Failure Surface G1 = 0

X1: − thickness of

comp. 1

[Lives at both hotspots > Limit]

Reliable Region

Failure Surface G2 = 0

[Life at 2nd hotspot=Limit]

Deterministic

Optimum

1st hotspot

2nd hotspot

X2: −

th

ick

ne

ss o

f

com

p.

2

Reliability-Based Design

Initial

Design


Design of Control Arm (US Army): Optimization Results

Initial

31.473

0.3235

1.0000

1.0000

1.0000

0.0050

1.0000

Optimum

32.717

0.9987

1.0000

1.0000

1.0000

0.9989

0.9991

Weight

R6

R43

R46

R80

R87

R42

Reliability

0 199.87%


X3 X4

X5

X7

X6

X2

X1


Design of Control Arm (US Army): Optimization Results

Initial

31.473

0.3235

1.0000

1.0000

1.0000

0.0050

1.0000

Optimum

32.717

0.9987

1.0000

1.0000

1.0000

0.9989

0.9991

Weight

R6

R43

R46

R80

R87

R42

Reliability

0 199.87%


Initial Stress

Contour

Final Stress

Contour

Optimum design

Initial design


Research Timeline

Reliability-

Based Design

Fatigue Life

PD

F

Optimized

Initial

Limit




PD

F

Health Index 1

He

alt

h I

nd

ex

2

Battery Prognostics


Scientist @ MDT / Faculty @ ISUPhD Student @ UMD

2007 2009 2011 2013 2015


Human Life-time

He

alt

h c

on

dit

ion

Human PHM Process


Perfectly healthy

Death limit

Medical treatment



An engineered system cannot manage itself.

It must be managed.


Health Management of Power Transformer

1 2 3 4 5 6

7 8 9 10 11 12

Health SensingHealth

ReasoningHealth

PrognosticsHealth

Management

1 3

2 4

Signal from Sensor 1

Signal from Sensor 2

Health Index for Failure Mode 6

Health Index for Failure Mode 9


PHM

Functions


Intelligent Prognostics Platform for Wind Turbine Gearbox


Raw sensory data

Health Sensing &

Data Acquisition

Condition-Based Control

and Maintenance

11

11

Health-relevant

feature

Frequency spectrum

Data Processing &

Feature Extraction

22

22

Gear RULBearing RUL

500 1000 1500 2000 25000

1

2

3

4

5

6×10-3

RUL Distributions

Time (days)

Health

Prognostics

44

44

Wind turbine

gearbox

Health

Diagnostics

33

Feature 1

Fe

atu

re 2

Health state map33

Degradation mode 1

Healthy

Mode 2


Research Timeline

Reliability-

Based Design

Fatigue Life

PD

F

Optimized

Initial

Limit




PD

F

Health Index 1

He

alt

h I

nd

ex

2

Battery Prognostics


Scientist @ MDT / Faculty @ ISUPhD Student @ UMD

2007 2009 2011 2013 2015


Li-Ion Battery in Implantable Medical Devices

Since 2004 Since 2010

Spinal Cord Stimulators

Mild electrical stimulation in the

spinal cord to alleviate chronic pain.

Deep Brain Stimulators

Targeted electrical stimulation to part of

brain for mitigating movement disorder.

Targeted longevity of 9 years, and 1000+ cycles

Inductively coupled recharge


Need to know more:

� Capacity every recharge cycle

Battery Prognostics

Do Patients/Physicians Need to Know More?

Patients/physicians are informed

of battery charge level

Remaining

capacityBOL capacity80%

Remaining

useful life 6 8 years months

Neurostimulator

Patient programmer

with antenna

Therapy screen

� Remaining use life during

annual check-up


Battery Prognostics

Schematic of Prognostics

Estimated capacity based on voltage and current measurements

Projected capacity Predicted end of life (EOL)

Particle filter used to

consider two sources of

uncertainty:

• Capacity estimation

• Model projection

70

80

90

100

0 100 200 300 400 500

No

min

al

Ca

pa

city

(%

)

Cycle Number

StatisticalLife Prediction

True Life

EOL limit

Hu C., Jain G., Tamirisa P., and Gorka T., “Method for Estimating Capacity and Predicting Remaining Useful Life of Lithium-Ion

Battery,” Applied Energy, v126, p182–189, 2014.


Particle Filter for Estimating Joint Distribution of Model Parameters

[Pitt and Shephard, 1999, Journal of the American Statistical Association]

Step 1: Evaluate

Importance Weights

Step 2: Selection

Likelihood function based on capacity estimates

Step 3: Sampling

X = {k1, k2, t0, mc}

Q(t,C) = k1(1-e-t/to) + k2t + mcC

Battery Prognostics




Battery Prognostics

0 [0] 200 [3.1] 400 [6.0] 600 [8.8]0

200

400

600

800

Cycle number [years on test]

RU

L (

cycle

s)

True RUL

Predicted RUL

0 [0] 200 [3.1] 400 [6.0] 600 [8.8]0

200

400

600

800


RU

L (

cycle

s)

True RUL

Predicted RUL

Life Predictions @ Multiple Cycles



Life Prediction @ Cycle 200


Norm

aliz

ed c

apacity (

%)

0 [0] 200 [3.1] 400 [6.0] 600 [8.8] 800 [11.5]70

75

80

85

90

95

100

Pre

dic

tio

n @

Cyc

le 2

00

Predicted

Life

Failure Limit

True Life

Real data


Wind Energy

Prognostics

and Health

Management

Design for

Failure

Prevention

Future Research Plan

Reliability-

Based Design

Design for

Functional

ReliabilityDesign for

Resilience

Energy Storage Student to be identified

• Reliability evaluation and

failure prognostics of new

materials

Ms. Kayla Johnson

(PhD Student)

• Intelligent prognostics

of wind turbine

gearbox


Q/A

Thank You!


Data Processing Health Diagnostics Health Prognostics

Fast Fourier transform Self-organizing map Similarity-based interpolation

Wavelet analysis Clustering analysis Bayesian linear regression

Principle component analysis Mahalanobis distance Particle Filter / MCMC

Expert feature extraction Support vector machine Ensemble prognostics

Statistical correlation (copula) Relevance vector machine Semi-supervised learning

Artificial neural networks K-Nearest Neighbor

Classification Fusion

Extended Kalman Filter

PHM Toolbox being Developed at Hu’s Lab


Journal Publications on PHM

1. Wang P., Youn B.D., and Hu C., “A Probabilistic Detectability-Based Sensor Network Design Method for System HealthMonitoring and Prognostics,” Journal of Intelligent Material Systems and Structures, DOI:10.1177/1045389X14541496, 2014. [ DOI ]

2. Hu C., Wang P., Youn B.D., and Lee W.R., “Copula-Based Statistical Health Grade System against Mechanical Faults ofPower Transformers,” IEEE Transactions on Power Delivery, v27, n4, p1809–1819, 2012. [ DOI ]

3. Youn B.D., Park K.M., Hu C., Yoon, J.T., and Bae Y.C., “Statistical Health Reasoning of Water-Cooled Power GeneratorStator Bars Against Moisture Absorption,” IEEE Transactions on Energy Conversion, vPP, p1–10, 2015. [ DOI ]

4. Hu C., Jain G., Schmidt C., Strief C., and Sullivan M., “Online Estimation of Lithium-Ion Battery Capacity Using SparseBayesian Learning,” Journal of Power Sources, v289, p105–113, 2015. [ DOI ]

5. Bai G., Wang P., and Hu C., “A Self-Cognizant Dynamic System Approach for Prognostics and Health Management,”Journal of Power Sources, v278, p163–174, 2015. [ DOI ]

6. Bai G., Wang, P., Hu C., and Pecht M., “A Generic Model-Free Approach for Lithium-ion Battery Health Management,”Applied Energy, v135, p247–260, 2014. [ DOI ]

7. Hu C., Jain G., Zhang P., Schmidt C., Gomadam P., and Gorka T., “Data-Driven Approach Based on Particle SwarmOptimization and K-Nearest Neighbor Regression for Estimating Capacity of Lithium-Ion Battery,” Applied Energy,v129, p49–55, 2014. [ DOI ]

8. Tamilselvan P., Wang P., and Hu C., “Health Diagnostics Using Multi-Attribute Classification Fusion,” EngineeringApplications of Artificial Intelligence, v32, p192–202, 2014. [ DOI ]

9. Hu C., Youn B.D., and Chung J., “A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC andCapacity Estimation,” Applied Energy, v92, p694–704, 2012. [ DOI ]

10. Hu C., Youn B.D., Kim T.J., and Wang P., “Semi-Supervised Learning with Co-Training for Data-Driven Prognostics,”Mechanical Systems and Signal Processing, v62–63, p75–90, 2015. [ DOI ]

11. Hu C., Jain G., Tamirisa P., and Gorka T., “Method for Estimating Capacity and Predicting Remaining Useful Life ofLithium-Ion Battery,” Applied Energy, v126, p182–189, 2014. [ DOI ]

12. Xi Z., Wang P., Rong Jing, and Hu C., “A Copula-Based Sampling Method for Data-Driven Prognostics,” ReliabilityEngineering and System Safety, DOI: 10.1016/j.ress.2014.06.014, 2014. [ DOI ]

13. Hu C., Youn B.D., and Wang P., “Ensemble of Data-Driven Prognostic Algorithms for Robust Prediction of RemainingUseful Life,” Reliability Engineering and System Safety, v103, p120–135, 2012. [ DOI ]

14. Wang P., Youn B.D., and Hu C., “A Generic Probabilistic Framework for Structural Health Prognostic and UncertaintyManagement,” Mechanical Systems and Signal Processing, v28, p622–637, 2012. [ DOI ]

Health

Sensing &

Data

Processing

Health

Diagnostics

Health

Prognostics

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