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

Sysoeva, Viktoriia
http://orcid.org/0000-0002-0912-8217
http://rave.ohiolink.edu/etdc/view?acc_num=miami1595978044573618

Abstract Details

2020, Master of Science, Miami University, Computational Science and Engineering.
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.
Mark Scott, Dr. (Advisor)
Chi-Hao Cheng, Dr. (Committee Member)
Peter Jamieson, Dr. (Committee Member)
98 p.
Computer Engineering; Electrical Engineering
Condition monitoring; DC-link capacitor; Machine Learning; SVM; ANN; Hidden Markov Model; DSP

Recommended Citations

Citations

  • Sysoeva, V. (2020). Hidden Markov Model-Supported Machine Learning for Condition Monitoring of DC-Link Capacitors [Master's thesis, Miami University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595978044573618

    APA Style (7th edition)

  • Sysoeva, Viktoriia. Hidden Markov Model-Supported Machine Learning for Condition Monitoring of DC-Link Capacitors. 2020. Miami University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=miami1595978044573618.

    MLA Style (8th edition)

  • Sysoeva, Viktoriia. "Hidden Markov Model-Supported Machine Learning for Condition Monitoring of DC-Link Capacitors." Master's thesis, Miami University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595978044573618

    Chicago Manual of Style (17th edition)

miami1595978044573618
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© 2020, all rights reserved.
This open access ETD is published by Miami University and OhioLINK.
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