## 4 Proposed Method  
### Health Management Process Based on Attention-MLGRU and PCA-SOM Algorithms  
To ensure the normal operation of a CNC machine tool servo drive system for a long time, a health management method of a machine tool servo drive system based on the Attention-MLGRU and PCA-SOM algorithms was proposed. The core of this method includes two parts, fault prediction and fault diagnosis. In the fault prediction stage, a multi-layer GRU neural network is used to predict the time sequence of the operating parameters, and the attention mechanism is introduced to establish the fault prediction model of the servo drive system. The introduction of the attention mechanism in GRU neural networks can improve the long-term dependence problem of GRU neural networks, improve model performance, handle variable-length input sequences, and provide interpretability and interpretability. This makes the model more flexible, accurate, and interpretable in sequence tasks. In the fault diagnosis stage, the SOM neural network is used for cluster analysis, and feature standardization and PCA are introduced into the SOM neural network to establish the fault diagnosis model of the servo drive system. The fault diagnosis effect of the model is better than that of the traditional method, and it can realize the dimensionality reduction analysis of high-dimensional fault data and can accurately identify the fault characteristics of the data, which solves the problem that the fault causes of the existing CNC machine tool servo drive control system are complicated and difficult to diagnose and provides a good idea for the fault diagnosis of the CNC machine tool servo drive system and improves the operation reliability of the machine tool. The specific process of health management is shown in Figure 8.  
Step 1: Use the workshop detailed manufacturing data and process system (MDC system) to monitor the actual production status of CNC machine tools and collect data for some key production parameters. Step 2: According to the production data of the field equipment of the MDC system, select appropriate data as the sample data set and conduct data preprocessing. The data preprocessing adopts the Gaussian filter method, and the processed data set is used as the input for fault prediction and fault diagnosis analysis. Step 3: Fault prediction  
(1) Initialization of parameters: Initialize the weight and bias of the multi-layer GRU neural network. (2) Forward propagation: For each time step, calculate the current input and the hidden state of the previous time step, as well as the attention weight. The input sequence is weighted and summed according to the attention weight to obtain the attention-weighted  
Figure 8: Health Management Flow Chart. representation. The attention-weighted representation is input into a multi-layer GRU, and the hidden state of the current time step is calculated. (3) Calculate the loss: Input the hidden state of the last time step into the output layer, calculate the predicted value, and calculate the loss function according to the predicted value and the target label. (4) Backpropagation: Calculate the gradient of the loss function to the predicted value and calculate the gradient of each parameter in the multi-layer GRU neural network through the backpropagation algorithm. (5) Use the Nadam algorithm to update network parameters, adjust weights and bias. Step 4: Fault diagnosis  
(1) Carry out feature standardization processing for different types of input sample data sets and determine the parameters of the output layer. (2) Establish an improved self-organizing neural network fault diagnosis model, input the feature standardized sample data set into the model, and conduct PCA dimensionality reduction to obtain the sample data after dimensionality reduction. (3) Conduct SOM cluster analysis on the sample data after dimensionality reduction and output the cluster analysis results. (4) Output fault diagnosis results according to cluster analysis results and determine the fault diagnosis accuracy rate.