Detection of Broken Rotor Bars in Induction Machines using Machine Learning Methods

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門（英文）

発行日: 2021/11/01

タイトル(英語): Detection of Broken Rotor Bars in Induction Machines using Machine Learning Methods

著者名: Stefan Quabeck (Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University), Wenbo Shangguan (Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University), Daniel Scharfenstein (Institute for Power Electr

著者名(英語): Stefan Quabeck (Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University), Wenbo Shangguan (Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University), Daniel Scharfenstein (Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University), Rik W. De Doncker (Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University)

キーワード: induction machine，broken rotor bar，fault detection，machine learning

要約(英語): Induction machines are used in a wide range of industrial applications due to their simplicity, ruggedness, and low price. Despite their robustness, induction machines eventually fail due to a variety of mechanisms. Most faults exhibit specific frequency components in the motor current spectrum, which allows for fault detection. Many classical fault detection methods have been developed for grid-connected machines with relatively fixed operating points. In inverter-driven machines with a wide operating range, these methods cannot reliably detect and classify faults. Machine learning methods have been successfully used for various classification tasks. This study therefore combines classical fault detection approaches with various fault classification algorithms to reliably detect induction machine faults over a wide operating range.The developed fault classification method is evaluated using steady-state measurements on an inverter-fed 5.5 kW induction machine. The algorithm shows promising fault detection and classification capabilities, achieving an accuracy of 97.4% over a wide load range.

本誌: IEEJ Journal of Industry Applications Vol.10 No.6 （2021） Special Issue on “ICEMS 2020-Hamamatsu”

本誌掲載ページ: 688-693 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/10/6/10_21000651/_article/-char/ja/