Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis. (January 2022)

• Record Type: Journal Article
• Authors: Feng, Yong; Chen, Jinglong; Zhang, Tianci; He, Shuilong; Xu, Enyong; Zhou, Zitong
• Is Part Of: ISA transactions. Volume 120(2022)
• Page Start: 383
• Page End: 401
• Publication Date: 2022-01
• Subjects: Fault diagnosis -- Semi-supervised learning -- Meta-learning -- Attention -- Few-shot classification; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.03.013
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In the engineering practice, lacking of data especially labeled data typically hinders the wide application of deep learning in mechanical fault diagnosis. However, collecting and labeling data is often expensive and time-consuming. To address this problem, a kind of semi-supervised meta-learning networks (SSMN) with squeeze-and-excitation attention is proposed for few-shot fault diagnosis in this paper. SSMN consists of a parameterized encoder, a non-parameterized prototype refinement process and a distance function. Based on attention mechanism, the encoder is able to extract distinct features to generate prototypes and enhance the identification accuracy. With semi-supervised few-shot learning, SSMN utilizes unlabeled data to refine original prototypes for better fault recognition. A combinatorial learning optimizer is designed to optimize SSMN efficiently. The effectiveness of the proposed method is demonstrated through three bearing vibration datasets and the results indicate the outstanding adaptability in different situations. Comparison with other approaches is also made under the same setup and the experimental results prove the superiority of the proposed method for few-shot fault diagnosis. Highlights: Propose semi-supervised meta-learning networks (SSMN) with squeeze-and-excitation attention for few-shot fault diagnosis. Generate discriminative features from raw inputs via attention mechanism. Refine prototypes for better fault identification by semi-supervised meta-learning. Combine SGD and Adam to converge the training loss efficiently for SSMN. Conduct extensive experiments to evaluate the effectiveness by bearing vibration datasets.