A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings. (15th September 2018)

• Record Type: Journal Article
• Authors: Haidong, Shao; Hongkai, Jiang; Ke, Zhao; Dongdong, Wei; Xingqiu, Li
• Is Part Of: Mechanical systems and signal processing. Volume 110(2018)
• Page Start: 193
• Page End: 209
• Publication Date: 2018-09-15
• Subjects: Tracking deep wavelet auto-encoder -- Intelligent fault diagnosis -- Electric locomotive bearings -- Gaussian wavelet function -- Adaptive tracking learning algorithm; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.03.011
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: Gaussian wavelet is used to design wavelet auto-encoder to enhance the quality of the learned features. A deep wavelet auto-encoder is constructed for higher-level feature learning and automatic fault diagnosis. An adaptive tracking learning algorithm is developed to further improve the diagnosis accuracy. Abstract: The condition monitoring of electric locomotive has attracted more and more attention due to its significance for improving the security, reliability and automation level. In this paper, a novel tracking deep wavelet auto-encoder (TDWAE) method is proposed for the intelligent fault diagnosis of electric locomotive bearings. Firstly, Gaussian wavelet function is adopted as the activation function to design wavelet auto-encoder (WAE), which can greatly enhance the quality of the features learned from the raw vibration signals of bearings. Secondly, a deep wavelet auto-encoder (DWAE) is constructed with several WAEs for higher-level feature learning and automatic fault diagnosis. Finally, an adaptive tracking learning algorithm is developed for flexibly determining the learning rate to further improve the diagnosis performance. The proposed method is applied to analyze the experimental vibration signals collected from electric locomotive bearings, and the results demonstrate that the proposed method is more effective than the traditional methods and standard deep auto-encoder.