Multireceptive Field Denoising Residual Convolutional Networks for Fault Diagnosis

Recent progress on intelligent fault diagnosis is mainly attributed to the explosive development of convolutional neural networks (CNNs). Many existing CNN-based fault diagnosis models can extract abundant features from the measured vibration signals but cannot explore enough discriminative features under strong noise conditions. This poses a challenge for industrial applications. To address this problem, we develop a new deep CNN model, called a multireceptive field denoising residual convolutional network (MF-DRCN). The major contributions are: a multireceptive field denoising (MFD) block is designed to enhance the deep features extracted by the CNN model and filter out the interference feature information; an adaptive feature integration (AFI) module is embedded in the CNN model to adaptively integrate features, so as to make better use of the extracted information; and an end-to-end CNN model called MF-DRCN is developed based on MFD and AFI. The experimental results demonstrate that the MF-DRCN has better feature extraction and antiinterference capabilities than the other seven competitive methods. Specifically, under strong noise conditions with SNR = -6 dB, the MF-DRCN achieves 84.51% and 86.45% diagnostic accuracy, respectively, on the planetary gearbox dataset and the industrial pump dataset, which suggests the MF-DRCN is a promising intelligent fault diagnosis approach.