Semi-supervised machine learning for motor eccentricity fault diagnosis

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Published Sep 4, 2023
Bingnan Wang Shen Zhang Hiroshi Inoue Makoto Kanemaru

Abstract

Eccentricity is one major indicator of mechanical faults in electric machines and needs to be detected early to avoid machine failures. Data-driven techniques based on machine learning and deep learning algorithms have been proposed in recent years for motor fault detection. However, majority of these methods use supervised learning algorithms and require large, labelled datasets, which can be challenging to obtain. In this paper, we propose a semi-supervised learning method based on a deep generative model using variational auto-encoder for eccentricity fault quantification. Good prediction accuracy can be achieved when only a small subset of training data has labels.

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Keywords

fault detection, eccentricity, semi-supervised learning

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Special Session Papers