Journal
ADVANCED ENGINEERING INFORMATICS
Volume 38, Issue -, Pages 91-100Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.aei.2018.06.006
Keywords
Bearing fault diagnosis; Feature selection; Similarity discriminant technique; SFART; Soft competitive learning; Parameter optimization
Funding
- National Natural Science Foundation of China [51575417, 51775391, 51605353]
- State Key Laboratory of Digital Manufacturing Equipment and Technology of China Open Project Fund [DMETKF2013010]
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In this work, a new classification method called Soft Competitive Learning Fuzzy Adaptive Resonance Theory (SFART) is proposed to diagnose bearing faults. In order to solve the misclassification caused by the traditional Fuzzy ART based on hard competitive learning, a soft competitive learning ART model is established using Yu's norm similarity criterion and lateral inhibition theory. The proposed SFART is based on Yu's norm similarity criterion and soft competitive learning mechanism. In SFART, Yu's similarity criterion and the lateral inhibition theory were employed to measure the proximity and select winning neurons, respectively. To further improve the classification accuracy, a feature selection technique based on Yu's norms is also proposed. In addition, Particle Swarm Optimization (PSO) is introduced to optimize the model parameters of SFART. Meanwhile, the validity of the feature selection technique and parameter optimization method is demonstrated. Finally, fuzzy ART/ ARTMAP (FAM) as well as the feasibility of the proposed SFART algorithm are validated by comparing the diagnosis effectiveness of the proposed algorithm with the classic Fuzzy c-means (FCM), Fuzzy ART and fuzzy ARTMAP (FAM).
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