# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
###### Abstract
[21] proposed a one-dimensional residual convolution neural network (1D-RCNN) that directly uses time-domain waveforms as input. [PERSON] et al. [22] used a residual network to fuse multiple sets of wavelet packet coefficients for fault diagnosis. [PERSON] et al. [23] proposed a multi-scale cluster-graph convolution neural network with a multi-channel residual network (MR-MCGCN), which can eliminate the effect of noise efficiently. Many researchers have found that the vibration signals of gearboxes have inherent multiscale features. [PERSON] et al. [24] proposed a new multiscale convolutional neural network (MSCNN) with coarse granularity strategy to effectively learn fault characteristics of different time scales. [PERSON] et al. [25] proposed a multiscale convolutional neural network with feature alignment (MSCNN-FA). [PERSON] et al. [26] proposed a multiscale kernel-based residual convolutional neural (MK-ResCNN) network. [PERSON] et al. [27] proposed a multi-scale cascaded midpoint residual convolutional neural network (MSC-MPResCNN). The above methods focus on a single fault or a compound fault of a single part. In the actual industrial scenario, faults often occur in multiple parts of the entire mechanical system at the same time, which brings a great challenge to fault diagnosis. A few researchers have been conducted on this problem. [PERSON] et al. [28] proposed the multivariate variational mode decomposition (MVMD) method, [PERSON] et al. [29] proposed an Adaptive-Projection with intrinsically transformed multivariate Empirical Mode decomposition (APIT-MEMD), and [PERSON] et al. [30] proposed a new morphological joint time-frequency adaptive kernel-based semi-smart framework. However, multi-fault diagnosis based on deep learning is rarely studied at present. In this paper, we propose an intelligent fault diagnosis method based on AMC-RSN to solve the above problems. Firstly, a channel attention mechanism module is constructedin the residual block and a soft thresholding function is introduced for noise reduction in the original signal. Then, a multi-channel network is constructed to fuse the feature information of each channel to extract as many features as possible. Hereinto, adaptive weights are set in each channel to obtain the most important information, and these weights are updated adaptively as the model is trained to obtain the best values. Finally, the MetACON activation function is used before the fully connected layer to decide whether to activate the neurons by the model outputs, which can improve the classification accuracy of the model. In summary, this paper has three main contributions as follows. 1. A new multi-channel residual network is proposed for extracting richer features in the signal, which solves the problem of insufficient effective feature extraction;
2. An adaptive learning method based on activation function is proposed to activate neurons adaptively, which can effectively avoid the interference of redundant features;
3. In the case of multiple faults in gearboxes, the experimental results show that the proposed method can effectively extract the features of the target faults and classify them accurately. The remainder of this paper is as follows. Section 2 presents the theory underlying the AMC-RSN of this paper. Section 3 presents the principles and architecture of the proposed AMC-RSN. In Section 4, the validation of the method and the comparison of related models are carried out and the results are discussed. Section 5 provides a full-text summary.