Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework. (April 2022)

• Record Type: Journal Article
• Authors: Li, Wei; Zhong, Xiang; Shao, Haidong; Cai, Baoping; Yang, Xingkai
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Modified ACGAN -- New framework -- Multi-mode data augmentation -- Rotating machinery fault diagnosis -- Spectrum normalization; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101552
• Languages: English
• ISSNs: 1474-0346

Abstract

Graphical abstract: Highlights: A new ACGAN framework for improving the quality of generation is developed. New loss functions by Wasserstein distance is used for smoothing training. Using spectral normalization to optimize the adversarial training process. The proposed MACGAN performs effective in bearing and gear fault diagnosis. Abstract: As one of the representative unsupervised data augmentation methods, generative adversarial networks (GANs) have the potential to solve the problem of insufficient samples in fault diagnosis of rotating machinery. However, the existing unsupervised GANs are usually incapable of simultaneously generating multi-mode fault samples and have some shortcomings such as mode collapse and gradient vanishing. To overcome these deficiencies, a supervised model called modified auxiliary classifier GAN (MACGAN) designed with new framework is proposed in this paper. Firstly, a new ACGAN framework is developed by adding an independent classifier to improve the compatibility between the classification and discrimination. Secondly, the Wasserstein distance is introduced in the new loss functions to overcome mode collapse and gradient vanishing. Finally, to achieve stable training, a spectral normalization is used to replace the weight clipping to constrain the weight parameters of discriminator. The proposed method is applied to fault diagnosis of bearing and gear. Compared with the existing GANs, the proposed method can more efficiently generate multi-mode fault samples with higher qualities, which can be used to assist the training of deep learning-based fault diagnosis models with high accuracy and good stability.