An optimal deep sparse autoencoder with gated recurrent unit for rolling bearing fault diagnosis. (22nd October 2019)

• Record Type: Journal Article
• Authors: Zhao, Ke; Jiang, Hongkai; Li, Xingqiu; Wang, Ruixin
• Is Part Of: Measurement science & technology. Volume 31:Number 1(2020)
• Publication Date: 2019-10-22
• Subjects: rolling bearing fault diagnosis -- hybrid deep learning model -- gated recurrent unit -- sparse autoencoder -- grey wolf optimizer; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ab3a59
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: The effective fault diagnosis of rolling bearings is of great importance in guaranteeing the normal operation of rotating machinery. However, measured rolling bearing vibration signals are highly nonlinear and interrupted by background noise, making it hard to obtain the representative fault features. Based on this, an optimal fault diagnosis method is proposed in this paper to accurately and steadily diagnose rolling bearing faults. The proposed method primarily contains the following stages. Firstly, a gated recurrent unit and a sparse autoencoder are constructed as a novel hybrid deep learning model to directly and effectively mine the fault information of rolling bearing vibration signals. Secondly, the key parameters of the constructed model are optimized by the grey wolf optimizer algorithm to achieve better diagnosis performance. Finally, the features obtained by the constructed model are input into the classifier to get the final diagnosis results. The proposed method is validated using the experimental and practical engineering bearing data and the results confirm that the diagnosis performance of the developed method is more effective and robust than other methods.