A new data fusion driven-sparse representation learning method for bearing intelligent diagnosis in small and unbalanced samples. (January 2023)

• Record Type: Journal Article
• Authors: Zhao, Yike; Zhang, Xin; Wang, Jiaxu; Wu, Lei; Liu, Zhiwen; Wang, Lei
• Is Part Of: Engineering applications of artificial intelligence. Volume 117:Part A(2023)
• Publication Date: 2023-01
• Subjects: Data fusion -- Sparse representation -- Fault diagnosis -- Discriminative dictionary learning -- Small samples -- Unbalanced samples; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105513
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Dictionary learning has made enormous achievements for its powerful feature representation capabilities. For the bearing fault diagnosis, the lack of failure samples is always a problem demanding prompt solutions. Since failure samples are far less than the normal samples, the data is inevitably to be unbalanced. To solve these problems, a new data fusion driven-sparse representation learning (DFDSRL) framework is proposed for fault diagnosis in small and unbalanced samples. The proposed method first constructs different dictionaries severally from the data samples grouped according to the signal modes (for example, the signals measured from the bearing under different working conditions). To induce the dictionary discriminability, discriminative sparse codes errors, reconstruction errors and classification errors are integrated as optimization objectives. Then, a new data fusion strategy is developed to fuse the dictionaries from all signal modes in the same fault class, and a fused dictionary for each fault class is generated for the final fault identification. The fusion is performed at the feature level instead of the conventional data level, and the discriminability of the fused dictionaries are further enhanced with the fusion strategy by eliminating the insignificant features shared by the atoms in each fault class dictionaries. Experimental results show that the DFDSRL achieves high fault identification accuracy for the problems of small and unbalanced samples in comparison with several advanced methods, benefitting from its excellent capability of fusing more fault features. By transforming the data unbalanced problem into the balanced small sample problem, DFDSRL further improves the fault identification accuracy.