A two-stage Fault Diagnosis Scheme of Partial Demagnetization Fault in PMSMs with Low Labelled Rates

被引：1

作者：

Mahmoud, Mahmoud S. ^{[1
]}

Khang, H. V. ^{[1
]}

Senanayaka, Jagath ^{[2
]}

Robbersmyr, Kjell G. ^{[1
]}

Panadero, Ruben ^{[3
]}

机构：

[1] Univ Agder, Dept Sci & Engn, Kristiansand, Norway

[2] Norwegian Univ Life Sci, Fac Sci & Technol, As, Norway

[3] Univ Politecn Valencia, Inst Energy Engn, Valencia, Spain

来源：

2023 IEEE INTERNATIONAL ELECTRIC MACHINES & DRIVES CONFERENCE, IEMDC | 2023年

关键词：

Anomaly detection; demagnetization diagnosis; finite element analysis (FEA); permanent magnet synchronous motors (PMSMs); semi-supervised machine learning (SSML); MAGNET; MOTORS;

D O I：

10.1109/IEMDC55163.2023.10238950

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study proposes a two-stage diagnosis scheme for partial demagnetization faults in permanent magnet synchronous motors (PMSMs) based on a small portion of faulty historical data and one-phase stator current analysis. The proposed method is based on applying anomaly detection in the first stage, and semi-supervised learning (SSML) combining self-training and a support vector machine (SVM) in the second stage to use the healthy, faulty and unlabelled data for improving the overall diagnostic accuracy. The effectiveness of the suggested scheme is evaluated through multiple datasets having different labels ratio, and compared with other supervised machine learning (SML) and SSML algorithms.

引用

页数：7

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