# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
###### Abstract  
Intelligent fault diagnosis is a hot research topic in machinery and equipment health monitoring. However, most intelligent fault diagnosis models have good performance in single fault mode, but poor performance in multiple fault modes. In real industrial scenarios, the interference of noise also makes it difficult for intelligent diagnostic models to extract fault features. To solve these problems, an adaptive multi-channel residual shrinkage network (AMC-RSN) is proposed in this paper. First, a channel attention mechanism module is constructed in the residual block and a soft thresholding function is introduced for noise reduction. Then, an adaptive multi-channel network is constructed to fuse the feature information of each channel in order to extract as many features as possible. Finally, the Meta-ACON activation function is used before the fully connected layer to decide whether to activate the neurons by the model outputs. The method was implemented in gearbox fault diagnosis, and the experimental results show that AMC-RSN has better diagnostic results than other networks under various faults and strong noises. deep learning; multi-fault; fault diagnosis; residual network; adaptive weights; gearbox +
Footnote †: journal: Applied  
neural networks (ANN) [7], support vector machines (SVM) [8], and k-means method [9], etc. The intelligent diagnosis steps of machine learning are divided into signal acquisition, feature extraction, and machine learning model classification. However, the difficulty of machine learning fault diagnosis lies in finding an effective feature [10]. Recently, with the development of artificial intelligence, deep learning (DL) methods have become a practical tool for fault diagnosis based on vibration signals [11]. Deep learning methods are machine learning methods with multi-level nonlinear transformations. However, in real industrial scenarios, vibration signals collected by the gearbox contain a lot of noise, and excessive noise will drown out the signal that characterizes the fault. In this case, a general DL mode can make a rough judgment. Therefore, minimizing the impact of noise on diagnostic models is the focus of current research. Several researchers have studied intelligent fault diagnosis in environments with strong noise and variable loads. [PERSON] et al. [12] proposed a hierarchical branching CNN (HB-CNN) method with excellent robustness in bearing fault diagnosis, [PERSON] et al. [13] proposed a training interference convolutional neural networks (TICNN) method that shows excellent performance in a noisy environment, and [PERSON] et al. [14] proposed a new method of integrating a learnable variational kernel into a one-dimensional CNN. To further improve the diagnostic performance of the network model, many researchers have given the model a stronger ability to extract features. [PERSON] et al. [15] proposed a broad convolutional neural network (BCNN) with certain incremental learning abilities. [PERSON] et al. [16] constructed a CNN-BGM model with a mixture of neural networks and Bayesian Gaussians. [PERSON] et al. [17] proposed a deeply coupled dense convolutional network (CDCN). Neural networks are generally optimized by a backpropagation algorithm which updates parameters by the gradient. Some networks obtain better performance by increasing the number of layers of the network, which tends to the disappearance of the gradient, and the underlying parameters cannot be updated. Deep residual networks (ResNet), a popular derivative of convolutional networks, use identity shortcuts to alleviate the difficulty of parameter optimization. In residual networks, optimization of the underlying parameters can be solved because gradients can be imported with identity shortcuts [18; 19]. [PERSON] et al. [20] proposed a dual-path mixed-domain residual threshold network (DP-MRTN) to improve the diagnostic performance of rolling bearings in high-noise environments. [PERSON] et al.