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

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.

Typical Power Grid Operation Mode Generation Based on Reinforcement Learning and Deep Belief Network

“…where x is the observed value of a characteristic in a certain operation mode, µ is the mean value of the characteristic data, σ is the standard deviation of the characteristic data, and x is the normalized data value. Then, Principal Component Analysis (PCA) [31] is used to compress the redundant dimensions of the high-dimensional operation mode data, the basic principle of which is to linearly transform the data and thus reduce the dimensionality under the condition of maintaining features with the largest variance in the sample points. The processing flow is as follows:…”

Improved Generative Adversarial Network for Bearing Fault Diagnosis with a Small Number of Data and Unbalanced Data

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