Case Studies: Prognostics and Health Management (PHM)

Prognostics and health management (PHM) technology has been successfully implemented into engineering practice in diverse settings. This chapter presents case studies that explain successful PHM practices in several engineering applications: (1) steam turbine rotors, (2) wind turbine gearboxes, (3) the core and windings in power transformers, (4) power generator stator windings, (5) lithium-ion batteries, (6) fuel cells, and (7) water pipelines. These examples provide useful findings about the four core functions of PHM technology, contemporary technology trends, and industrial values.

[1]  Bhaskar Saha,et al.  An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries , 2010 .

[2]  Puqiang Zhang,et al.  Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery , 2014 .

[3]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[4]  Bo-Suk Yang,et al.  Application of Dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals , 2006 .

[5]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[6]  Seongjun Lee,et al.  Complementary Cooperation Algorithm Based on DEKF Combined With Pattern Recognition for SOC/Capacity Estimation and SOH Prediction , 2012, IEEE Transactions on Power Electronics.

[7]  Luo Yongfen,et al.  Research on extraction technique of transformer core fundamental frequency vibration based on OLCM , 2006, IEEE Transactions on Power Delivery.

[8]  M. Pecht,et al.  Review of offshore wind turbine failures and fault prognostic methods , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

[9]  Bo-Suk Yang,et al.  Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..

[10]  Gregory L. Plett,et al.  Recursive approximate weighted total least squares estimation of battery cell total capacity , 2011 .

[11]  David Infield,et al.  Online wind turbine fault detection through automated SCADA data analysis , 2009 .

[12]  Fouad Slaoui-Hasnaoui,et al.  Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges , 2014 .

[13]  K N Gupta.  Vibration — A tool for machine diagnostics and condition monitoring , 1997 .

[14]  L. Guzzella,et al.  Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries , 2010 .

[15]  Peter Sandborn,et al.  A Methodology for Determining the Return on Investment Associated With Prognostics and Health Management , 2009, IEEE Transactions on Reliability.

[16]  Kevin R. Wheeler,et al.  A Model-Based Probabilistic Inversion Framework for Characterizing Wire Fault Detection Using TDR , 2011, IEEE Transactions on Instrumentation and Measurement.

[17]  Yong-Chae Bae,et al.  Datum unit optimization for robustness of a journal bearing diagnosis system , 2015 .

[18]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[19]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[20]  Yong Chae Bae,et al.  Wet bar detection by using water absorption detector , 2008 .

[21]  Qiang Miao,et al.  Health monitoring of hard disk drive based on Mahalanobis distance , 2011, 2011 Prognostics and System Health Managment Confernece.

[22]  Robert I. Damper,et al.  A fast separability-based feature-selection method for high-dimensional remotely sensed image classification , 2008, Pattern Recognit..

[23]  Taejin Kim,et al.  TDR-based pipe leakage detection and location using Bayesian inference , 2015, 2015 IEEE Conference on Prognostics and Health Management (PHM).

[24]  Michael Buchholz,et al.  Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods , 2013 .

[25]  Tom Gorka,et al.  Method for estimating capacity and predicting remaining useful life of lithium-ion battery , 2014, 2014 International Conference on Prognostics and Health Management.

[26]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[27]  Wei He,et al.  State of charge estimation for electric vehicle batteries using unscented kalman filtering , 2013, Microelectron. Reliab..

[28]  L. Bertling,et al.  Maintenance Management of Wind Power Systems Using Condition Monitoring Systems—Life Cycle Cost Analysis for Two Case Studies , 2007, IEEE Transactions on Energy Conversion.

[29]  Ralph E. White,et al.  Capacity Fade Mechanisms and Side Reactions in Lithium‐Ion Batteries , 1998 .

[30]  Taejin Kim,et al.  An Online-Applicable Model for Predicting Health Degradation of PEM Fuel Cells With Root Cause Analysis , 2016, IEEE Transactions on Industrial Electronics.

[31]  Dirk Uwe Sauer,et al.  Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination , 2013 .

[32]  Jae Sik Chung,et al.  A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2010 .

[33]  Mark W. Verbrugge,et al.  Adaptive, multi-parameter battery state estimator with optimized time-weighting factors , 2007 .

[34]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[35]  Krishna R. Pattipati,et al.  System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[36]  Hongwen He,et al.  A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .

[37]  Anoushiravan Farshidianfar,et al.  Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine , 2007 .

[38]  Michael Pecht,et al.  A generic model-free approach for lithium-ion battery health management , 2014 .

[39]  Chao Hu,et al.  Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[40]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[41]  Kwok L. Tsui,et al.  A naive Bayes model for robust remaining useful life prediction of lithium-ion battery , 2014 .

[42]  Darryll J. Pines,et al.  Planetary Transmission Diagnostics , 2004 .

[43]  Michael J. Brennan,et al.  A model of the correlation function of leak noise in buried plastic pipes , 2004 .

[44]  Sevket Demirci,et al.  Ground penetrating radar imaging of water leaks from buried pipes based on back-projection method , 2012 .

[45]  Wook-Ryun Lee,et al.  Copula-Based Statistical Health Grade System Against Mechanical Faults of Power Transformers , 2012, IEEE Transactions on Power Delivery.

[46]  Jean-Michel Vinassa,et al.  Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks , 2012 .

[47]  Byeng D. Youn,et al.  A probabilistic detectability-based sensor network design method for system health monitoring and prognostics , 2015 .

[48]  Yi-Hsien Chiang,et al.  Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electr , 2011 .

[49]  Hee Soo Kim,et al.  Statistical Health Reasoning of Water-Cooled Power Generator Stator Bars Against Moisture Absorption , 2015, IEEE Transactions on Energy Conversion.

[50]  José R. Perán,et al.  Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation , 2011 .

[51]  Tian Han,et al.  Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals , 2006 .

[52]  Robert B. Randall,et al.  Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation) , 2005 .

[53]  Yeon-Whan Kim,et al.  Omnidirectional regeneration (ODR) of proximity sensor signals for robust diagnosis of journal bearing systems , 2017 .

[54]  Kai Goebel,et al.  Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .

[55]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

[56]  Qiang Miao,et al.  Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .

[57]  Hee Soo Kim,et al.  Health diagnostics of water-cooled power generator stator windings using a Directional Mahalanobis Distance (DMD) , 2013, 2013 IEEE Conference on Prognostics and Health Management (PHM).

[58]  Yoshiyuki Inoue,et al.  Technology for detecting wet bars in water-cooled stator windings of turbine generators , 2003, IEEE International Electric Machines and Drives Conference, 2003. IEMDC'03..

[59]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .