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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
K. Chen, W. Wang, F. Zhang, J. Liang, and K. Yu, “Correlation-guided particle swarm optimization approach for feature selection in fault diagnosis,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125306
Citation: K. Chen, W. Wang, F. Zhang, J. Liang, and K. Yu, “Correlation-guided particle swarm optimization approach for feature selection in fault diagnosis,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125306
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Correlation-Guided Particle Swarm Optimization Approach for Feature Selection in Fault Diagnosis

doi: 10.1109/JAS.2025.125306
Funds:  This work was supported in part by the National Natural Science Foundation of China (62206255, 62476254, 62176238, U23A20340), Young Talents Lifting Project of Henan Association for Scienceand Technology (2024HYTP023), Natural Science Foundation of Henan Province (222300420088), Frontier Exploration Projects of Longmen Laboratory (LMQYTSKT031), Program for Science & Technology Innovation Talents in Universities of Henan Province (23HASTIT023), and Key Research and Development Program of Henan (241111210100)
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    Abstract
  • A large number of features are involved in fault diagnosis, and it is challenging to identify important and relative features for fault classification. Feature selection selects suitable features from the fault dataset to determine the root cause of the fault. Particle swarm optimization (PSO) has shown promising results in performing feature selection due to its promising search effectiveness and ease of implementation. However, most PSO-based feature selection approaches for fault diagnosis do not adequately take domain-specific a priori knowledge into account. In this study, we propose a correlation-guided PSO feature selection approach for fault diagnosis that focuses on improving the initialisation effectiveness, individual exploration ability, and population diversity. To be more specific, an initialisation strategy based on feature correlation is designed to enhance the quality of the initial population, while a probability individual updating mechanism is proposed to improve the exploitation ability. In addition, a sample shrinkage strategy is developed to enhance the ability to jump out of local optimal. Results on four public fault diagnosis datasets show that the proposed approach can select smaller feature subsets to achieve higher classification accuracy than other state-of-the-art feature selection methods in most cases. Furthermore, the effectiveness of the proposed approach is also verified by examining real-world fault diagnosis problems.

     

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