A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem. (February 2022)

• Record Type: Journal Article
• Authors: Dong, Yunjia; Li, Yuqing; Zheng, Huailiang; Wang, Rixin; Xu, Minqiang
• Is Part Of: ISA transactions. Volume 121(2022)
• Page Start: 327
• Page End: 348
• Publication Date: 2022-02
• Subjects: Rolling element bearings -- Intelligent fault diagnosis -- Transfer learning -- Dynamic model -- Convolutional neural network -- Small sample; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.03.042
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Intelligent fault diagnosis of rolling element bearings gains increasing attention in recent years due to the promising development of artificial intelligent technology. Many intelligent diagnosis methods work well requiring massive historical data of the diagnosed object. However, it is hard to get sufficient fault data in advance in real diagnosis scenario and the diagnosis model constructed on such small dataset suffers from serious overfitting and losing the ability of generalization, which is described as small sample problem in this paper. Focus on the small sample problem, this paper proposes a new intelligent fault diagnosis framework based on dynamic model and transfer learning for rolling element bearings race faults. In the proposed framework, dynamic model of bearing is utilized to generate massive and various simulation data, then the diagnosis knowledge learned from simulation data is leveraged to real scenario based on convolutional neural network (CNN) and parameter transfer strategies. The effectiveness of the proposed method is verified and discussed based on three fault diagnosis cases in detail. The results show that based on the simulation data and parameter transfer strategies in CNN, the proposed method can learn more transferable features and reduce the feature distribution discrepancy, contributing to enhancing the fault identification performance significantly. Highlights: A bearing diagnosis framework via simulation auxiliary and parameter transfer for small sample problem. Exploring effectiveness under diverse transfer strategies. Leveraging transferable knowledge from simulation data to real scenario. Robust to CNN architectures with different complexity.