Hidden Markov Model-Supported Machine Learning for Condition Monitoring of Dc-Link Capacitors

ABSTRACT HIDDEN MARKOV MODEL-SUPPORTED MACHINE LEARNING FOR CONDITION MONITORING OF DC-LINK CAPACITORS by Viktoriia Sysoeva Power electronics are critical components in society’s modern infrastructure. In electrified vehicles and aircraft, losing power jeopardizes personal safety and incur financial penalties. Because of these concerns, many researchers explore condition monitoring (CM) methods that provide real-time information about a system’s health. This thesis develops a CM method that determines the health of a DC-link capacitor in a three-phase inverter. The approach uses measurements from a current transducer in two Machine Learning (ML) algorithms, a Support Vector Machine (SVM), and an Artificial Neural Network (ANN), that classify the data into groups corresponding to the capacitor’s health. This research evaluates six sets of data: time-domain, frequency-domain, and frequency-domain data subjected to four smoothing filters: the moving average with a rectangular window (MARF) and a Hanning window, the locally weighted linear regression, and the Savitzky– Golay filter. The results show that both ML algorithms estimate the DC-link capacitor health with the highest accuracy being 91.8% for the SVM and 90.7% for the ANN. The MARF-smoothed data is an optimal input data type for the ML classifiers due to its low computational cost and high accuracy. Additionally, a Hidden Markov Model increases the classification accuracy up to 98% when utilized with the ANN. HIDDEN MARKOV MODEL-SUPPORTED MACHINE LEARNING FOR CONDITION MONITORING OF DC-LINK CAPACITORS A Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Science by Viktoriia Sysoeva Miami University Oxford, Ohio 2020 Advisor: Dr. Mark J. Scott Co-Advisor: Dr. Chi-Hao Cheng Reader: Dr. Peter Jamieson ©2020 Viktoriia Sysoeva This Thesis titled HIDDEN MARKOV MODEL-SUPPORTED MACHINE LEARNING FOR CONDITION MONITORING OF DC-LINK CAPACITORS by Viktoriia Sysoeva has been approved for publication by The College of Engineering and Computing and Department of Electrical and Computer Engineering ____________________________________________________ Dr. Mark J. Scott ______________________________________________________ Dr. Chi-Hao Cheng _______________________________________________________ Dr. Peter Jamieson Table of Contents CHAPTER 1 INTRODUCTION ................................................................................................. 1 1.1 RELIABILITY AND MAINTENANCE ASPECTS OF POWER ELECTRONIC APPLICATIONS ... 1 1.2 PROBLEM STATEMENT .................................................................................................... 4 1.3 PROPOSED APPROACH ..................................................................................................... 5 1.4 THESIS OBJECTIVES ......................................................................................................... 7 1.5 LIST OF THE CHAPTERS ................................................................................................... 7 CHAPTER 2 LITERATURE REVIEW ..................................................................................... 9 2.1 CONDITION MONITORING METHODS IN POWER ELECTRONICS ...................................... 9 2.1.1 Signature Analysis ...................................................................................................... 9 2.1.2 Artificial Intelligence ................................................................................................ 13 2.2 CONDITION MONITORING METHODS FOR DC-LINK CAPACITORS ................................ 14 2.3 SUMMARY ...................................................................................................................... 18 CHAPTER 3 BACKGROUND ................................................................................................. 20 3.1 DEGRADATION BEHAVIOR OF METALLIZED POLYPROPYLENE FILM CAPACITORS ...... 20 3.2 E-PHM SYSTEM FOR DC-LINK CAPACITOR CONDITION MONITORING ........................ 23 3.3 DIGITAL SIGNAL PROCESSING ....................................................................................... 26 3.4 MACHINE LEARNING ..................................................................................................... 30 3.5 HIDDEN MARKOV MODEL ............................................................................................. 33 3.6 SUMMARY ...................................................................................................................... 35 CHAPTER 4 EXPERIMENTAL RESULTS ........................................................................... 37 4.1 CONDITION MONITORING PROCEDURE ......................................................................... 37 4.2 CAPACITOR CLASSES ..................................................................................................... 38 4.3 EXPERIMENTAL SETUP AND DATA ACQUISITION ......................................................... 41 4.4 DATA PREPROCESSING AND FEATURE SELECTION ....................................................... 42 4.5 MACHINE LEARNING CLASSIFICATION ......................................................................... 45 4.6 HMM-BASED OUTPUT CORRECTION ............................................................................ 48 4.7 SUMMARY ...................................................................................................................... 55 CHAPTER 5 CONCLUSION AND FUTURE WORK .......................................................... 56 5.1 CONCLUSION ................................................................................................................. 56 5.2 FUTURE WORK .............................................................................................................. 58 REFERENCES ............................................................................................................................. 60 APPENDIX .................................................................................................................................... 70 iii List of Tables Table 1.1: Applications, Goals, and Advantages of Condition Monitoring ....................... 4 Table 2.1: Accuracy of Capacitor Ripple-Current Sensor-Based Methods ...................... 16 Table 2.2: Accuracy of Circuit Model-Based Methods .................................................... 17 Table 2.3: Accuracy of Data and Advanced Algorithm-Based Methods ......................... 18 Table 4.1: DC-link Capacitor Class Parameters ............................................................... 39 Table 4.2: E-PHM Maintenance Actions .......................................................................... 40 Table 4.3: Operational Parameters of the Three-phase Inverter ....................................... 41 Table 4.4: Data Acquisition Parameters ........................................................................... 42 Table 4.5: Preprocessing Stages ....................................................................................... 42 Table 4.6: ML Classification Performance ....................................................................... 46 Table 4.7: Number of Operations per Sample for Smoothing Filters with 1000-point Window ............................................................................................................................. 48 Table 4.8: HMM-supported ML Classification Performance ........................................... 55 Table 5.1: Comparison of Data Types .............................................................................. 57 Table 5.2: Three-phase Inverter Operational Parameters in Future Experiments ............ 59 iv List of Figures Fig. 1.1: Estimated world MRO spending by the airline industry per specified years in billion USD. ........................................................................................................................ 1 Fig. 1.2: Locations of debris found after the Guildford’s explosion inside an underframe equipment [4]. ..................................................................................................................... 2 Fig. 1.3: Annual and cumulative growth in U.S. wind power capacity [6]. ....................... 3 Fig. 1.4: E-PHM system diagram. ...................................................................................... 6 Fig. 2.1: Classification of capacitor CM methods. ........................................................... 14 Fig. 3.1: Capacitor equivalent circuit. ............................................................................... 20 Fig. 3.2: Temperature characteristic of capacitance for MPPF capacitors [97]. ............... 21 Fig. 3.3: Frequency characteristic of impedance for MPPF capacitors [97]. ................... 21 Fig. 3.4: Frequency characteristic of impedance for new and aged capacitors [58]. ........ 23 Fig. 3.5: Lumped-parameter model of a three-phase inverter with an R-L load. ............. 24 Fig. 3.6: Simplified high-frequency model to estimate the EMI (a) before and (b) after S2 turns on (S3 and S5 are on for both states). ....................................................................... 25 Fig. 3.7: Simulation results for five capacitor classes: (a) EMI spectrum near the resonant frequency and (b) current peaks for different capacitor classes. ...................................... 26 Fig. 3.8: ANN architecture diagram. ...............................................................................

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