A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern. (July 2023)

• Record Type: Journal Article
• Authors: Xia, Pengcheng; Huang, Yixiang; Tao, Zhiyu; Liu, Chengliang; Liu, Jie
• Is Part Of: Reliability engineering & system safety. Volume 235(2023)
• Publication Date: 2023-07
• Subjects: Digital twin -- Fault diagnosis -- Motor -- Semi-supervised learning -- Transfer learning; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2023.109256
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: 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. Highlights: A novel digital twin-enhanced semi-supervised motor fault diagnosis. Knowledge transfer from multi-physics motor digital twin model is proposed. A novel phase-contrastive current dot pattern for efficient current processing. An inter-space sample generation method for continuous feature manifold learning. A clustering-based metric learning for semi-supervised performance enhancing.