## 6 Conclusions  
This paper presents a health management system based on an improved gated recurrent neural network (Attention-MLGRU) and improved self-organizing mapping neural network (PCA-SOM) and realizes the health management of a computer numerically  
Figure 22: (**H1**) Scatter diagram of PCA-SOM fault analysis for multiple fault data; (**H2**) PCA-SOM fault analysis pie chart for multiple fault data; (**H3**) Fault diagnosis result diagram; (**H4**) Diagnostic result confusion matrix. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Elapsed Running Time(s)** & **Recognition Accuracy (\%)** \\ \hline PCA-SOM & 7.46 & 99.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison results of SOM and PCA-SOM for a large sample. controlled (CNC) machine tool servo drive system based on this method. The health management system mainly includes two parts, which are fault prediction stage and fault diagnosis stage. In the fault prediction stage, the gated recurrent unit (GRU) neural network is adopted as the prediction algorithm, a multi-layer GRU neural network prediction model is established, and the attention mechanism is introduced into the GRU neural network to carry out weighted processing of information at different positions in the sequence data, which improves the long-term dependence problem of the GRU neural network and improves the model performance. It also provides interpretability and explainability. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model and makes it suitable for solving the prediction problem of large-scale data. In the fault diagnosis stage, based on the traditional self-organizing mapping (SOM) neural network method, feature standardization and principal component analysis (PCA) are introduced into the SOM neural network data preprocessing part, which solves the problem that the traditional SOM neural network struggles to analyze large data samples and improves the accuracy and efficiency of fault diagnosis. Different from common fault diagnosis techniques, the PCA-SOM method can reduce the dimensionality of original data features, while retaining more features of original data points, which greatly improves the ability of this method to process large data samples. In addition, this method enhances the characteristics of fault data, makes fault data easy to distinguish, and solves the problem that the traditional fault diagnosis method has poor diagnosis effect when the fault characteristics are fuzzy. It is worth noting that this method not only predicts and evaluates the difference between the fault and the health state of the machine tool servo drive system but also accurately identifies the fault type, which provides help for the follow-up maintenance work and the formulation of maintenance strategy. Finally, the validity of the health management method is tested with the equipment operation parameter data set containing fault data. The results show that the health management system can accurately predict and identify the fault information and can realize the health management of the machine tool servo drive system. The future research direction is mainly to improve the generalization ability and recognition ability of the model. In the manufacturing industry, due to the complexity and variability of the production environment, data changes are particularly common, for example, the aging of equipment, the replacement of materials, and the fine tuning of process parameters may lead to changes in data distribution. Therefore, improving the generalization ability of the model is particularly important for fault prediction and diagnosis. At the same time, in the fault data, there may be a serious imbalance between normal samples and fault samples, which will cause the model to prefer to learn common normal patterns in the training process, while the recognition ability of rare fault modes is weak.