Lebesgue-Sampling-Based Diagnosis and Prognosis for Lithium-Ion Batteries

Traditional fault diagnosis and prognosis (FDP) approaches are based on periodic sampling, i.e., samples are taken and algorithms are executed both in a periodic manner. As the volume of sensor data and complexity of algorithms keep increasing, the bottleneck of FDP is mainly the limited computational resources, which is particularly true for distributed applications where FDP functions are deployed on microcontrollers and embedded systems with limited computation resources. This paper introduces the concept of Lebesgue sampling (LS) in FDP and proposes a LS-based FDP (LS-FDP) framework. In the proposed LS-FDP, a novel diagnostic philosophy of “execution only when necessary” is developed in computation cost reduction. For prognosis, different from traditional approaches in which the prognostic horizon is on the time axis, the proposed approach defines the prognostic horizon on the fault state axis. With a reduced prognostic horizon, the LS-FDP naturally benefits the uncertainty management. The goal of this paper is to create the fundamental knowledge for LS-FDP solutions that are cost efficient, capable for the deployment on systems with limited computation sources, and supportive to the trend of distributed FDP schemes in complex systems. The design and implementation of particle-filter-based LS-FDP are presented with experimental results on lithium-ion batteries to verify the effectiveness of the proposed approaches.

[1]  J. Celaya,et al.  Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation , 2012 .

[2]  Bin Zhang,et al.  Blind Deconvolution Denoising for Helicopter Vibration Signals , 2008, IEEE/ASME Transactions on Mechatronics.

[3]  Pavle Boškoski,et al.  Bearing fault detection with application to PHM Data Challenge , 2011 .

[4]  N. de Freitas Rao-Blackwellised particle filtering for fault diagnosis , 2002, Proceedings, IEEE Aerospace Conference.

[5]  Hamid-Reza Bahrami,et al.  Iterative Condition Monitoring and Fault Diagnosis Scheme of Electric Motor for Harsh Industrial Application , 2015, IEEE Transactions on Industrial Electronics.

[6]  Vicente Climente-Alarcon,et al.  Rotor-Bar Breakage Mechanism and Prognosis in an Induction Motor , 2015, IEEE Transactions on Industrial Electronics.

[7]  George Vachtsevanos,et al.  A novel blind deconvolution de-noising scheme in failure prognosis , 2010 .

[8]  George Vachtsevanos,et al.  Methodologies for uncertainty management in prognostics , 2009, 2009 IEEE Aerospace conference.

[9]  Xiao-Sheng Si,et al.  An Adaptive Prognostic Approach via Nonlinear Degradation Modeling: Application to Battery Data , 2015, IEEE Transactions on Industrial Electronics.

[10]  Bin Zhang,et al.  A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection , 2011, IEEE Transactions on Industrial Electronics.

[11]  Bin Zhang,et al.  Application of Blind Deconvolution Denoising in Failure Prognosis , 2009, IEEE Transactions on Instrumentation and Measurement.

[12]  Hao Ye,et al.  Fault diagnosis of networked control systems , 2006 .

[13]  Danwei Wang,et al.  An Integrated Approach to Prognosis of Hybrid Systems With Unknown Mode Changes , 2015, IEEE Transactions on Industrial Electronics.

[14]  Roy McCann,et al.  Lebesgue Sampling with a Kalman Filter in Wireless Sensors for Smart Appliance Networks , 2008, 2008 IEEE Industry Applications Society Annual Meeting.

[15]  Jonathan DeCastro,et al.  Autonomous Vehicle Battery State-of-Charge Prognostics Enhanced Mission Planning , 2020 .

[16]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[17]  G. Kacprzynski,et al.  Advances in uncertainty representation and management for particle filtering applied to prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[18]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[19]  Liang Tang,et al.  Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries , 2013, IEEE Transactions on Industrial Electronics.

[20]  Xiaofeng Wang,et al.  Fault Diagnosis and Prognosis Based on Lebesgue Sampling , 2014 .

[21]  Nando de Freitas,et al.  Real-Time Monitoring of Complex Industrial Processes with Particle Filters , 2002, NIPS.

[22]  George J. Vachtsevanos,et al.  Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems , 2010 .

[23]  Selin Aviyente,et al.  Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings , 2015, IEEE Transactions on Industrial Electronics.

[24]  Hao Ye,et al.  Fault diagnosis of networked control systems , 2007, Annu. Rev. Control..

[25]  Jose A. Antonino-Daviu,et al.  Advanced Induction Motor Rotor Fault Diagnosis Via Continuous and Discrete Time–Frequency Tools , 2015, IEEE Transactions on Industrial Electronics.

[26]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[27]  Afshin Izadian,et al.  Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries , 2015, IEEE Transactions on Industrial Electronics.

[28]  Abhinav Saxena,et al.  - 1-A COMPARISON OF THREE DATA-DRIVEN TECHNIQUES FOR PROGNOSTICS , 2008 .

[29]  B. Saha,et al.  Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques , 2008, 2008 IEEE Aerospace Conference.

[30]  Kai Goebel,et al.  A Survey of Artificial Intelligence for Prognostics , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[31]  K. Åström,et al.  Comparison of Riemann and Lebesgue sampling for first order stochastic systems , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[32]  Steven X. Ding,et al.  A Review on Basic Data-Driven Approaches for Industrial Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.

[33]  S. R. Wells,et al.  Sliding mode control applied to reconfigurable flight control design , 2002 .

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

[35]  Danwei Wang,et al.  Short-Circuit Fault Diagnosis for Three-Phase Inverters Based on Voltage-Space Patterns , 2014, IEEE Transactions on Industrial Electronics.

[36]  Irem Y. Tumer,et al.  A SURVEY OF AIRCRAFT ENGINE HEALTH MONITORING SYSTEMS , 1999 .

[37]  Weizhong Yan,et al.  Defect classification of highly noisy NDE data using classifier ensembles , 2006, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[38]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..