Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty

Baokun Han,Guifang Liu,Sixiang Jia,Jinrui Wang

doi:10.1155/2020/8836477

Abstract

Recently, generative adversarial networks (GANs) are widely applied to increase the amounts of imbalanced input samples in fault diagnosis. However, the existing GAN-based methods have convergence difficulties and training instability, which affect the fault diagnosis efficiency. This paper develops a novel framework for imbalanced fault classification based on Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), which interpolates randomly between the true and generated samples to ensure that the transition region between the true and false samples satisfies the Lipschitz constraint. The process of feature learning is visualized to show the feature extraction process of WGAN-GP. To verify the availability of the generated samples, a stacked autoencoder (SAE) is set to classify the enhanced dataset composed of the generated samples and original samples. Furthermore, the exhibition of the loss curve indicates that WGAN-GP has better convergence and faster training speed due to the introduction of the gradient penalty. Three bearing datasets are employed to verify the effectiveness of the developed framework, and the results show that the proposed framework has an excellent performance in mechanical fault diagnosis under the imbalanced training dataset.

Highlights

Machine fault diagnosis plays a significant role in ensuring the normal and orderly operation of industrial production.ere are usually two main methodologies for fault diagnosis: physic-based and data-driven-based methods
Case 1: Case Western Reserve University Dataset. is section uses the bearing fault data of Case Western Reserve University (CWRU) to validate the proposed fault diagnosis framework. e frequency of sampling is 48 kHz. e bearing is damaged by the electrical discharge machining (EDM) single point and divided into four health conditions: normal, inner ring fault, outer ring fault, and rolling body fault. ere are three types of damage dimensions for each type of fault: 0.18 mm, 0.36 mm, and 0.54 mm, for a total of 10 healthy types of bearing datasets. ey are named as NC, IF1, IF2, IF3, OF1, OF2, OF3, RF1, RF2, and RF3
For Wasserstein GAN (WGAN)-GP-stacked autoencoder (SAE), there are only several samples being misclassified in Figure 7(c), which is much better than the other two methods. erefore, we can conclude that the developed framework has the strongest feature extraction and feature classification capabilities than the other two methods

Summary

Introduction

Machine fault diagnosis plays a significant role in ensuring the normal and orderly operation of industrial production. In practice, mechanical equipment often run in the normal state for a long time so the monitor data have a limited capacity of rare fault types These methods often fail to perform well in the unbalanced fault diagnosis problem and even fail to identify rare fault categories. The proposed method generates simulation samples through Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) [28] for fault types with fewer data. It interpolates randomly between the true and generated samples to ensure that the transition region between the true and false samples satisfies the Lipschitz constraint [29].

Theoretical Background

Case 1

Case 2

Findings

Conclusion