Deep residual learning-based fault diagnosis method for rotating machinery. (December 2019)

• Record Type: Journal Article
• Authors: Zhang, Wei; Li, Xiang; Ding, Qian
• Is Part Of: ISA transactions. Volume 95(2019)
• Page Start: 295
• Page End: 305
• Publication Date: 2019-12
• Subjects: Fault diagnosis -- Rotating machinery -- Residual learning -- Rolling bearing -- Convolutional neural network; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2018.12.025
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach. Highlights: Large neural network can be efficiently trained in fault diagnosis. Machinery signal with variable sequential length can be processed. Accurate testing diagnosis results are obtained with small variance. Little prior expertise on signal processing and fault diagnosis is required.