A Robust Deep Learning Network for Low-Speed Machinery Fault Diagnosis Based on Multikernel and RPCA

Abstract

Machinery fault diagnosis is an attractive but challenging task, especially for low-speed conditions. Therefore, a new discriminative approach that introduces robust principal component analysis (RPCA) and multikernel to deep neural networks is proposed to perform intelligent fault diagnosis. First, RPCA is applied to extract fault signals from extreme background noise based on its sensitivity to grossly corrupted data. Second, two cascaded multikernel principal component analysis stages with additional robustness to distortions in feature extraction are used to enhance the energy of spectrum symptom and overcome the tricky issues of low-speed machinery. Especially, the multikernel is introduced into the basic PCA filters to learn the data-adapting convolution filter and gain additional robustness to nonlinearity in the signal. Finally, the proposed method is demonstrated on signals from laboratory tests (with a slightly damaged defect in a bearing) and structural fault data, outperforming those of traditional machine learning and classical deep learning methods. Moreover, hidden information of the network is visualized to analyze the reasons for its high performance.