Hilbert-Huang変換を利用した電動機軸受の異常検出

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2021/10/01

タイトル(英語): Bearing Fault Detection in Induction Motors with Hilbert-Huang Transformation

著者名: 月間　満（大阪電気通信大学），安枝　裕希（大阪電気通信大学）

著者名(英語): Mitsuru Tsukima (Osaka Electro-communication University), Yuki Yasueda (Osaka Electro-communication University)

キーワード: 誘導電動機，軸受，電流徴候解析，Hilbert-Huang変換 induction motors，bearings，current signature analysis，Hilbert-Huang transformation

要約(英語): Currently, induction motors are widely used in many industrial applications owing to their simple construction and high reliability. There has been a gradual increase in the demands for on-line health monitoring and fault detection techniques to avoid unscheduled maintenance and economic losses caused by sudden failures. In this study, we focused on the detections of bearing faults, which are the most frequently occurring faults in electric motors, and we adopted the Hilbert-Huang transformation, which is a recent signal processing technique, to analyze non-linear or non-stationary signals. We prepared several bearing samples with varying degrees of anomalies by heat treatments and built them into the motors. Based on the measurements of the stator current waveform to the motors, we determined the factors related to the degree of anomalies of the bearings, by comparing their loudnesses. Consequently, we confirmed that the components of higher-order (more than 3rd order) intrinsic mode functions have a positive correlation with the degree of anomalies of the bearings.

本誌: 電気学会論文誌D（産業応用部門誌） Vol.141 No.10 （2021）

本誌掲載ページ: 812-817 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/141/10/141_812/_article/-char/ja/