# Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN-GCE Model
## 1 Introduction  
The reliability of rotating equipment and machinery is crucial to the modern industrial sector. It affects production, safety, and income [1, 2]. Therefore, timely and accurate monitoring and diagnosis of rotating machinery failures is necessary [3]. Rolling bearings are a key component of rotating machines, and their failure directly affects stability and safety. Therefore, it is important to perform fault diagnosis [4]. Scholars have developed a number of fault detection methods using the traditional method by extracting manually the signal characteristics [5, 6]. Although these methods are helpful, they need to extract fault features manually which is time-consuming and strongly depend on professional signal processing knowledge that leads to poor diagnostic efficiency [7]. Intelligent fault diagnosis methods that use deep learning to diagnose faults have been a growing trend with the rapid advancement of artificial intelligence, which includes Auto Encoder, AE [8], Convolutional Neural Network (CNN) [9], Deep BeliefNetwork (DBN) [10], Recurrent Neural Network (RNN) [11], Deep Transfer Learning (DTL) [12], and so on. The Convolutional Neural Network (CNN), one of the most representative deep-learning algorithms, is widely used for fault diagnosis due to its powerful feature mapping abilities, and automatically learns fault features [13]. The CNN model can automatically extract useful information from the samples. Convolutional Neural Networks (CNNs), usually improve the performance of a model by increasing its depth [14]. However, as the network deepens, the accuracy of the model may instead decrease due to poor parameter updating during the flow of gradients. For this reason, [PERSON] et al. [15] proposed the deep residual network (ResNet) that centers on the use of residual connections in the network to avoid gradient vanishing and exploding for better parameter optimization. Recently, ResNet has been used in the field of fault detection. For example, [PERSON] et al. [16] used ResNet in order to combine multiple wavelet coefficients. [PERSON] et al. [17] developed a ResNet technique based on hybrid attention mechanisms and used it for fault diagnosis in wind turbine gearboxes. [PERSON] et al. [18] combined ResNet with migration learning and achieved good performance. These models may have improved diagnostics to a certain extent. However, the vibration signals from real-world industrial fields (such as CNC machines, wind turbines, etc.) are usually noisy, and this can blur fault characteristics, reducing the effectiveness of the model learning features [19]. [PERSON] et al. [20] developed a Deep residual shrinkage network (DRSN) based on the residual network to deal with noise's impact on fault diagnosis. The Deep Residual Shrinkage Network (DRSN), based on the residual network, introduces an attentive mechanism as well as a threshold soft function to reduce noise. The network improves feature learning under high noise levels and is also applicable to pattern recognition in various noise-containing fields. [PERSON] et al. [21] proposed an improved Deep Residue Shrinkage Network to address the problem of signal distortion caused by noise reduction. [PERSON] et. al. [22] proposed an improved version of DRSN, replacing the soft threshold by a leaky one for better model performance. [PERSON] et al. [23] introduced spatial attention into DRSN to capture the spatial dependence of the feature maps, thus constructing a model that considers a hybrid attention mechanism with multiple channels. [PERSON] [24] et al. further optimized a bearing failure diagnosis model based upon DRSN-CW. This achieves a more efficient temporal features extraction and classification by combining a Long-Short-Term Memory Network (LSTM), and an attention mechanism. Vibration, acoustic, temperature, and current signals [25; 26] are commonly used as signal sources in engineering practice. The above methods of fault diagnosis for rolling bearings are often blended with noise, which can mask the characteristic of the signal.