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Continual learning classification method and its application to equipment fault diagnosis

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Abstract

Classification methods play a significant role in the fault diagnosis field. However, they cannot effectively recognize new types of fault data and improve their classification performance timely through learning the testing data, for they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system, we propose a continual learning classification method (CLCM) and apply it to equipment fault diagnosis. During the testing stage, it continually cultivates new memory cells and new types of memory cells through learning the testing data to improve its classification performance. It classifies the known types of data and clusters the new types of data. Experimental evidence on six well-known datasets from the UCI repository and ball bearing test data verified its effectiveness and superiority. Results show that it has better classification performance when the testing data include all types of data, and it outperforms the other methods when the testing data include new types of data, especially new types of labeled data. The fewer types of training data, the more advantages it has.

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Acknowledgments

This research was funded by the National Natural Science Foundation of China (Grant No. 52075310, 51575331).

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Authors and Affiliations

  1. School of Petroleum Engineering, Changzhou University, Changzhou, 213164, People’s Republic of China

    Dong Li

  2. Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Dong Li & Furong Gao

  3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, People’s Republic of China

    Shulin Liu & Xin Sun

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  1. Dong Li
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Correspondence to Dong Li.

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Li, D., Liu, S., Gao, F. et al. Continual learning classification method and its application to equipment fault diagnosis. Appl Intell 52, 858–874 (2022). https://doi.org/10.1007/s10489-021-02455-7

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