Analysis And Research On Mathematical Model Of Health Diagnosis Of Cnc Machine Tool Spindle System Based On Machine Learning

The CNC machine tool has been widely used in the field of machinery manufacturing as a work machine,and the spindle system is one of the core components of the machine tool,and its health status directly affects the overall processing performance of the machine tool.Therefore,it is of great significance to carry out the relevant analysis and research on the health status monitoring and health status evaluation of the machine tool spindle system,and identify the early fault symptoms and fault diagnosis,which is of great significance to ensure the machining accuracy and processing quality of the machine tool.This paper mainly studies the relationship between the vibration signal output by the spindle system and the health status of the spindle system during the working process of the machine tool,and establishes a health diagnosis analysis model to implement the health diagnosis of the CNC machine tool spindle system.The main research contents are as follows:(1)Analyze the factors that affect the health status of the spindle system,and study the output status signal when the spindle system fails.By comparing the manifestations of the status signal when the fault occurs,a spindle system health diagnosis method based on vibration signals is proposed.(2)Analyze and study the vibration characteristics of the spindle system in healthy and abnormal states,propose a VMD-MWPE signal processing and feature extraction method for the vibration signal output by the spindle system,and design experiments to collect experimental data to verify this.Effectiveness of Signal Processing and Feature Extraction Methods.(3)Establish the relationship model between the vibration signal feature information and the spindle system state,and use the SVM model to identify and judge the feature information.In order to improve the accuracy of judgment,particle swarm optimization(PSO)is introduced to optimize the parameters of SVM.The established model is verified by experimental data,and the results show that the PSO-SVM diagnostic model has high recognition and judgment accuracy and is relatively stable.(4)A comprehensive analysis platform for CNC machine tool health diagnosis was built using Python,and the overall architecture,data acquisition module,data processing module,diagnostic service module,and human-computer interaction interface of the system were designed.The platform can realize the functions of dynamic monitoring,fault analysis,status data display and information release and early warning of CNC machine tools.