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

Transcription

1 Toward Reliable Engineered System Design: RELIABILITY-BASED DESIGN AND PROGNOSTICS AND HEALTH MANAGEMENT(PHM) Chao Hu Assistant Professor Department of Mechanical Engineering Iowa State University 1

2 Motivation Boeing 787 Dreamliner fire due to overheated Li-ion battery Japan Airlines (JAL) in Boston, Jan Consequence: Over $1.1 million daily loss due to groundings I-35W Bridge collapse due to faulty design, Aug Consequence: 13 deaths, 145 injured, $60 million loss Wind turbine collapse due to faulty maintenance, Feb Consequence: Collapse of whole wind turbine 2

3 Motivation Power transformer fire due to faulty bushing, Jul Consequence: $5 million property & business loss UPS flight fire possibly due to overheated Li-ion battery, Feb 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? 3

4 Research Timeline PhD UMD MDT / ISU Prognostics and Health Management (PHM) Sensing Reasoning Prognostics Reliability- Based Design Health Index 2 Health Index 1 PDF Remaining Useful Life (RUL) PDF Limit Optimized Initial Battery Prognostics Fatigue Life Li-Ion Rechargeable Deep Brain Stimulators 4

5 Research Timeline PhD UMD MDT / ISU Photo credit Inside a Lithium-Ion Battery Metal particle ( ) Electrode Separator (+) Electrode 5

6 Reliability-Based Design Design of Control Arm (US Army): Methodology [Life at 1 st hotspot=limit] Failure Surface G 1 = 0 Deterministic Optimum 1 st hotspot X 2 : thickness of comp. 2 Initial Design [Lives at both hotspots > Limit] Reliable Region 2 nd hotspot 0 X 1 : thickness of comp. 1 Failure Surface G 2 = 0 [Life at 2 nd hotspot=limit] 6

7 Reliability-Based Design Design of Control Arm (US Army): Optimization Results Initial Optimum Weight Reliability R R 42 R 43 X 3 X 4 X 2 R 46 X 7 X 1 R X 5 X 6 R % 0 1 7

8 Reliability-Based Design Design of Control Arm (US Army): Optimization Results Initial Optimum Weight Reliability R R 42 Initial design Initial Stress Contour R 43 R 46 R Final Stress Contour R Optimum design 99.87% 0 1 8

9 Research Timeline PhD UMD MDT / ISU Prognostics and Health Management (PHM) Sensing Reasoning Prognostics Reliability- Based Design Health Index 2 Health Index 1 PDF Remaining Useful Life (RUL) PDF Limit Optimized Initial Battery Prognostics Fatigue Life Li-Ion Rechargeable Deep Brain Stimulators 9

10 Prognostics and Health Management (PHM) Human PHM Process Health condition Medical treatment Death limit Perfectly healthy Human Life-time 10

11 Prognostics and Health Management (PHM) An engineered system cannot manage itself. It must be managed. 11

12 Prognostics and Health Management (PHM) Health Management of Power Transformer Signal from Sensor 1 Signal from Sensor Health Index for Failure Mode 6 Health Index for Failure Mode 9 Health Sensing Health Reasoning Health Prognostics Health Management PHM Functions 12

13 Prognostics and Health Management (PHM) Intelligent Prognostics Platform for Wind Turbine Gearbox Wind turbine gearbox Condition-Based Control and Maintenance 1 Health Sensing & Data Acquisition 2 Data Processing & Feature Extraction 3 Health Diagnostics 4 Health Prognostics 1 Raw sensory data 2 Frequency spectrum 3 Health state map 4 RUL Distributions Health-relevant feature Feature 2 Healthy Degradation mode 1 Feature 1 Mode Gear RUL Bearing RUL Time (days) 13

14 Research Timeline PhD UMD MDT / ISU Prognostics and Health Management (PHM) Sensing Reasoning Prognostics Reliability- Based Design Health Index 2 Health Index 1 PDF Remaining Useful Life (RUL) PDF Limit Optimized Initial Battery Prognostics Fatigue Life Li-Ion Rechargeable Deep Brain Stimulators 14

15 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 cycles Inductively coupled recharge 15

16 Battery Prognostics Do Patients/Physicians Need to Know More? Patients/physicians are informed of battery charge level Therapy screen Need to know more: Capacity every recharge cycle Remaining capacity 80% BOL capacity Remaining use life during annual check-up Remaining useful life 6 years 8 months Patient programmer with antenna Neurostimulator 16

17 Battery Prognostics Schematic of Prognostics Estimated capacity based on voltage and current measurements Projected capacity Predicted end of life (EOL) Nominal Capacity (%) EOL limit True Life Cycle Number Statistical Life Prediction Particle filter used to consider two sources of uncertainty: Capacity estimation Model projection Hu C.,Jain G., TamirisaP., and GorkaT., Method for Estimating Capacity and Predicting Remaining Useful Life of Lithium-Ion Battery, Applied Energy, v126, p ,

18 Battery Prognostics Particle Filter for Estimating Joint Distribution of Model Parameters [Pitt and Shephard, 1999, Journal of the American Statistical Association] Q(t,C)=k 1 (1-e -t/to )+ k 2 t +m c C X= {k 1, k 2,t 0,m c } Step 1: Evaluate Importance Weights Step 2: Selection Likelihood function based on capacity estimates Step 3: Sampling Hu C.,Jain G., TamirisaP., and GorkaT., Method for Estimating Capacity and Predicting Remaining Useful Life of Lithium-Ion Battery, Applied Energy, v126, p ,

19 Battery Prognostics Life Cycle 200 Life Multiple Cycles Normalized capacity (%) Real data Cycle 200 Failure Limit Predicted Life True Life RUL (cycles) True RUL Predicted RUL 70 0 [0] 200 [3.1] 400 [6.0] 600 [8.8] 800 [11.5] Cycle number [years on test] 0 0 [0] 200 [3.1] 400 [6.0] 600 [8.8] Cycle number [years on test] Hu C.,Jain G., TamirisaP., and GorkaT., Method for Estimating Capacity and Predicting Remaining Useful Life of Lithium-Ion Battery, Applied Energy, v126, p ,

20 Future Research Plan Energy Storage Student to be identified Reliability evaluation and failure prognostics of new materials Design for Reliability- Functional Based Design Reliability Prognostics Design for and Failure Health Management Prevention Design for Resilience Wind Energy Ms. Kayla Johnson (PhD Student) Intelligent prognostics of wind turbine gearbox 21

21 Thank You! Q/A 22

22 PHM Toolbox being Developed at Hu s Lab 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 23

23 Journal Publications on PHM 1. Wang P., Youn B.D., and Hu C., A Probabilistic Detectability-Based Sensor Network Design Method for System Health Monitoring and Prognostics, Journal of Intelligent Material Systems and Structures, DOI: / X , 2014.[ DOI] 2. Hu C., Wang P., Youn B.D., and Lee W.R., Copula-Based Statistical Health Grade System against Mechanical Faults of Power Transformers, IEEE Transactions on Power Delivery, v27, n4, p , 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 Generator Stator 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 Sparse Bayesian Learning, Journal of Power Sources, v289, p , 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, p , 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, p , 2014.[ DOI] 7. Hu C., Jain G., Zhang P., Schmidt C., Gomadam P., and Gorka T., Data-Driven Approach Based on Particle Swarm Optimization 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, Engineering Applications of Artificial Intelligence, v32, p , 2014.[ DOI] 9. Hu C., Youn B.D., and Chung J., A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation, Applied Energy, v92, p , 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 of Lithium-Ion Battery, Applied Energy, v126, p , 2014.[ DOI] 12. Xi Z., Wang P., Rong Jing, and Hu C., A Copula-Based Sampling Method for Data-Driven Prognostics, Reliability Engineering and System Safety, DOI: /j.ress , 2014.[ DOI] 13. Hu C., Youn B.D., and Wang P., Ensemble of Data-Driven Prognostic Algorithms for Robust Prediction of Remaining Useful Life, Reliability Engineering and System Safety, v103, p , 2012.[ DOI] 14. Wang P., Youn B.D., and Hu C., A Generic Probabilistic Framework for Structural Health Prognostic and Uncertainty Management, Mechanical Systems and Signal Processing, v28, p , 2012.[ DOI] Health Sensing & Data Processing Health Diagnostics Health Prognostics 24