Bearing Fault Diagnosis Using ACWGAN-GP Enhanced by Principal Component Analysis

Bin Chen, Chengfeng Tao, Jie Tao, Yuyan Jiang () and Ping Li
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Bin Chen: School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243002, China
Chengfeng Tao: School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243002, China
Jie Tao: School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243002, China
Yuyan Jiang: School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243002, China
Ping Li: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Sustainability, 2023, vol. 15, issue 10, 1-16

Abstract: Rolling bearings are one of the most widely used parts in all kinds of rotating machinery (including wind power equipment) and also one of the most easily damaged parts, which makes fault diagnosis of rolling bearings a promising research field. To this end, recent studies mainly focus on fault diagnosis cooperating with deep learning. However, in practical engineering, it is very challenging to collect massive fault data, resulting in low accuracy of bearing fault classification. To solve the problem, an auxiliary classifier optimized by a principal component analysis method is proposed to generate an adversarial network model in which Wasserstein distance and gradient penalty are used to improve the stability of the network training process in case of over-fitting and gradient disappearance during model training. Specifically, we implement the model system using two main components. First, the one-dimensional time domain signal is transformed into a two-dimensional grayscale image and the principal component analysis is employed to reduce the dimension of the original data; this is instead of random noise as the input of the generator thereby preserving the characteristics of the original data to a certain extent. Second, in a generative adversarial network, the label information of the fault data is inserted into the generator to achieve supervised learning, thereby improving the data generation performance and reducing the training time cost. The experimental results show that our model could produce high-quality samples that are similar to real samples and that it could significantly improve the classification accuracy of fault diagnosis in the case of insufficient fault samples.

Keywords: rolling bearings; fault diagnosis; data augmentation; auxiliary classifier generative adversarial networks; principal component analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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