A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern

Xia Pengcheng; Huang Yixiang; Tao ZhiyuLiu ChengliangLiu Jie

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A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern

机译：A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern

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摘要

Motor plays a core role in most industrial equipment. Accurate fault diagnosis of motor is a critical task and intelligent data-driven methods have gained significant advances. However, to obtain sufficient labeled data to train the models is expensive and laborious in industrial applications, and how to utilize three-phase current signals efficiently is a challenging task. To deal with these problems, a digital twin-enhanced semi -supervised framework is proposed for label-scarce motor fault diagnosis. First, a precise motor digital twin model is established based on multi-physics simulation and knowledge transfer is performed from the virtual space to the physical space. Second, a novel phase-contrastive current dot pattern (PCCDP) representation is proposed to transform three-phase motor stator current to a gray-scale image with an ordered arrangement and then characteristics of three phases can be contrasted in tight regions for efficient processing. Third, inter -space sample generation is proposed for continuous feature manifold learning to tackle discrepancy between spaces. Finally, intra-space sample generation and a clustering-based metric learning are also introduced to improve semi-supervised fault diagnosis performance. An induction motor fault experiment is conducted and a digital twin model is built correspondingly. Experiments verify the effectiveness and superiority of the proposed framework.

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来源
《Reliability engineering & system safety》 |2023年第7期|1.1-1.15|共15页
作者
Xia Pengcheng; Huang Yixiang; Tao ZhiyuLiu ChengliangLiu Jie;
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作者单位

Shanghai Jiao Tong Univ||Shanghai Jiao Tong Univ;

Carleton Univ;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
Digital twin; Fault diagnosis; Motor; Semi-supervised learning; Transfer learning;

A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern

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