An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case

The rapid development of deep learning raises a new research area for condition monitoring and fault diagnosis of mechanical equipment recently. However, the amount of labeled fault samples is limited in industrial field, also the samples are filled with complex environmental noise. Thus, a model with strong generalization and robustness is required. To tackle these challenges, an adversarial denoising convo-lutional neural network (ADCNN) is proposed in this paper. Moving maximum is firstly applied to the frequency spectrum of the vibration signal to enhance the anti-noise performance of the training sam-ples. Then the enhanced training samples are erased with dynamic probability to simulate noise inter-ference. Meanwhile, adversarial training is utilized to expand the labeled samples until Nash equilibrium is reached. These processes improve the robustness and generalization of ADCNN, and avoid over-fitting with limited amount of labeled samples. In our two experiments, when signal to noise ratio(SNR) is -12dB, the ADCNN achieves a diagnosis accuracy of 94.05% and 94.47%, respectively. Besides, ADCNN can maintain a high diagnosis accuracy under limited sample size case, and has the best adaptation perfor-mance across different load domains. The comparison studies with respect to other models demonstrate the applicability and superiority of ADCNN. (C) 2020 Elsevier B.V. All rights reserved.