Intelligent fault diagnosis under small sample size conditions via Bidirectional InfoMax GAN with unsupervised representation learning

The abnormal detection of rotating machinery under small sample size conditions is of prime importance in the field of fault diagnosis. In this work, we proposed an unsupervised representation learning method called Bidirectional InfoMax GAN (BIMGAN), which can perform fast and effective feature extraction and fault recognition with few samples. First, we obtain the low-dimensional feature representation by a prior normalized encoder and reconstruction of the sample via the generator. Second, the mapping relationship between the sample and its corresponding feature representation is learned by maximizing mutual information estimation with the constraint of the feature matching (FM) strategy. Different from the general GANs, we are aiming at learning a good feature mapping of an encoder to capture the feature representation instead of reconstructing realistic samples. And then, a supervised pattern recognition task based on the feature representation is conducted for fault diagnosis. Finally, the inverse mapping learned by the encoder is visualized and the effectiveness is demonstrated. And the performance of the proposed method outperforms several advanced unsupervised methods on two case studies of rolling bearings fault recognition with some standard architectures, where the average accuracy can achieve 99.73% and 98.36% respectively. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this study, an unsupervised representation learning method called BIMGAN was proposed for fault diagnosis of rotating machinery under small sample size conditions. By maximizing mutual information estimation and feature matching strategy, the method learns the mapping relationship between samples and their feature representations, achieving average accuracies of 99.73% and 98.36% in case studies.