Fault diagnosis of rolling bearing based on optimized soft competitive learning Fuzzy ART and similarity evaluation technique. (October 2018)

• Record Type: Journal Article
• Authors: Wan, Xiao-Jin; Liu, Licheng; Xu, Zengbing; Xu, Zhigang; Li, Qinglei; Xu, Fengxiang
• Is Part Of: Advanced engineering informatics. Volume 38(2018)
• Page Start: 91
• Page End: 100
• Publication Date: 2018-10
• Subjects: Bearing fault diagnosis -- Feature selection -- Similarity discriminant technique -- SFART -- Soft competitive learning -- Parameter optimization; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.06.006
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Soft competitive learning Fuzzy Adaptive Resonance Theory (SFART) model is proposed. The Yu's norm similarity measure is utilized to improve feature selection. SFART model was established by Yu's norm similarity criterion and lateral inhibition theory. A parameter optimization method based on Particle Swarm Optimization (PSO) is proposed. Fuzzy c-means (FCM), Fuzzy ART, and fuzzy ARTMAP (FAM) are compared with the proposed SFART. Abstract: 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).