The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network

Xuejun Liu, Wei Sun, Hongkun Li, Zeeshan Hussain and Aiqiang Liu
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Xuejun Liu: School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China
Wei Sun: School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China
Hongkun Li: School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China
Zeeshan Hussain: School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China
Aiqiang Liu: School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China

Energies, 2022, vol. 15, issue 13, 1-20

Abstract: The rolling bearing is a critical part of rotating machinery and its condition determines the performance of industrial equipment; it is necessary to detect rolling bearing faults as early as possible. The traditional methods of fault diagnosis are not efficient and are time-consuming. With the help of deep learning, the convolution neural network (CNN) plays a huge role in the data-driven methods of bearing fault diagnosis. However, the vibration signal is non-stationary, contains high noise, and is one-dimensional, which is difficult to analyze directly by the CNN model. Considering the multi-domain learning as an advantage of deep learning, this paper proposes a novel rolling bearing fault diagnosis approach using an improved one-dimensional (1D) and two-dimensional (2D) convolution neural network (CNN) of two-domain information learning. The constructed fault diagnosis model combining 1D and 2D CNN extracts the fault features from the two-domain information of bearing fault samples. The padding and dropout technology are utilized to fully extract features from the raw data and reduce over-fitting. To prove the validity of the proposed method, this paper performs two tests with two bearing datasets, the Case Western Reserve University (CWRU) bearing dataset and the Dalian University of Technology (DUT) vibration laboratory dataset. The experimental results show that our proposed method achieves high recognition accuracy of rolling bearing fault states via two-domain learning of monitoring data, and there is no manual experience necessary. Vibration data under strong noise were also used to test the method, and the results show the superiority and robustness of the proposed method.

Keywords: rolling bearing; fault diagnosis; convolution neural network; multi-domain supervised learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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