Fault diagnosis and interpretability method for rotating machinery based on generative adversarial networks

Abstract

Abstract Fault diagnosis methods based on deep learning often require a large amount of labeled data to train the network. However, in engineering applications, fault data collection is limited, and deep learning methods lack interpretability, which restricts the effectiveness of fault diagnosis models. This paper introduces a novel residual convolutional neural network transformer Eigen-class activation mapping generative adversarial network (RCTECAM GAN) to address the problem of class imbalance and the interpretability of neural networks. Firstly, residual convolutional neural network-transformer is designed as the generator architecture, which comprehensively captures the features of raw one-dimensional time-series signals from both global and local perspectives, thereby improving the quality of generated samples. Secondly, Eigen-class activation mapping is embedded in the classifier to highlight key regions that significantly contributing to the final diagnostic results, introducing interpretability into RCTECAM GAN. Experimental results demonstrate that the proposed method exhibits excellent performance in handling data class imbalance, achieving a fault diagnosis accuracy of 99.31%, which outperforms existing methods in addressing data class imbalance and enhancing the interpretability of deep learning models. This provides new ideas for solving the problems of class imbalance and interpretability of neural networks in the field of fault diagnosis.