Abstract
A multidomain variance-learnable prototypical network (MVPN) is proposed to learn transferable knowledge from a large-scale dataset containing sufficient samples of multiple faults for few-shot diagnosis of novel faults (i.e., disjoint with fault types in the large-scale dataset). Signal characterizations in time, frequency, and time–frequency domains are first constructed to make full use of information contained in severely limited labeled data. Mahalanobis distance is proposed as a criterion for improving classification performance by considering different spreads between classes in the embedding space. The spread variance of each class is learned by constructing an additional deep learning network in the original prototypical network. Multidomain signals are used to learn the prototype representations and spread variances separately, and are finally fused for classification. With the proposed MVPN, deeper variance-learnable embedding learning from wider domain characterizations improves the ability of few-shot fault diagnosis. Experiments are conducted to evaluate the performance of MVPN using datasets collected from a benchmark bearing and a Delta 3-D printer. Results indicate that the proposed MVPN performs competitively compared to state-of-the-art few-shot learning algorithms.
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Acknowledgements
This research is partially supported by the National Natural Science Foundation of China (72171049, 52175080, 52005103 and 52205556), Young Elite Scientists Sponsorship Program by CAST (2022QNRC001), the Guangdong Basic and Applied Basic Research Foundation (2022A1515012004, 2021A1515110521 and 2020A1515110139), Department of Education of Guangdong in China (2021ZDJS083). The valuable comments and suggestions from the editors and the three anonymous reviewers are very much appreciated.
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Long, J., Chen, Y., Huang, H. et al. Multidomain variance-learnable prototypical network for few-shot diagnosis of novel faults. J Intell Manuf 35, 1455–1467 (2024). https://doi.org/10.1007/s10845-023-02123-2
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DOI: https://doi.org/10.1007/s10845-023-02123-2