A spiking neural network-based approach to bearing fault diagnosis. (October 2021)

• Record Type: Journal Article
• Authors: Zuo, Lin; Zhang, Lei; Zhang, Zhe-Han; Luo, Xiao-Ling; Liu, Yu
• Is Part Of: Journal of manufacturing systems. Volume 61(2021)
• Page Start: 714
• Page End: 724
• Publication Date: 2021-10
• Subjects: Spiking neural network -- Third generation neural network -- Intelligent fault diagnosis -- Bearing fault diagnosis -- Local mean decomposition; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.07.003
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: The spiking neural network(SNN) is tailored as an intelligent fault diagnosis tool. The features are encoded into spikes to train an SNN with tempotron learning rules. The experimental results show a promising accuracy of the proposed method. Abstract: Fault diagnosis, with the aim of accurately identifying the presence of various faults as early as possible so at to provide effective information for maintenance planning, has been extensively concerned in advanced manufacturing systems. With the increase of the amount of condition monitoring data, fault diagnosis methods have gradually shifted from the model-based paradigm to data-driven paradigm. Intelligent fault diagnosis approaches which can automatically mine useful information from a huge amount of raw data are becoming promising ways to identify faults of manufacturing systems in the context of massive data. In this paper, the Spiking Neural Network (SNN), as the third generation neural network, is tailored as an intelligent fault diagnosis tool for bearings in rotating machinery. Compared to the perceptron and the back propagation neural network (BPNN) which are respectively the first and second generations of neural networks. SNN, which introduces the concept of time into its operating model can more closely mimic natural neural networks and possesses high bionic characteristics. In the proposed SNN-based approach to bearing fault diagnosis, features extracted from raw vibration signals through the local mean decomposition (LMD) are encoded into spikes to train an SNN with the improved tempotron learning rule. The performance of the proposed method is examined by the CWRU and MFPT datasets, and the experimental results show that the method can achieve a promising accuracy in bearing fault diagnosis.