Research On Fault Diagnosis Of Rolling Bearing Of CNC Machine Tool Based On Web

As the "industrial master machine" of modern manufacturing,CNC machine tools play a pivotal role in modern manufacturing.Whether a country's industrial level is advanced or not is reflected in CNC machine tools to a certain extent.With the rapid development of numerical control technology,the quality,accuracy and efficiency of machined parts have been greatly improved,but the problems that have followed have been the increasing complexity and precision of CNC machine tools,the structure is more complex.Besides the difficulty of fault diagnosis of core components is also increasing.As one of the core components of a CNC machine tool,a rolling bearing often directly affects the performance of the entire CNC machine tool.However,the current traditional bearing fault diagnosis systems usually can not find bearing faults accurately and timely,and failed to meet the needs of enterprise information development.By analyzing and studying the rolling bearing fault characteristics of CNC machine tools,this paper designs and develops a web-based CNC lathe rolling bearing fault diagnosis system based on the characteristics of fault characteristic signals.The main research work is as follows:(1)The decomposition result of traditional variational modal analysis(VMD)in bearing fault diagnosis is mainly affected by the number of components K and the penalty factor ?,which leads to the problem of low analytical power.To address this issue,a method for fault diagnosis of rolling bearing is proposed based on bacterial foraging algorithm(BFA)to optimize VMD parameters.The analysis of the experimental results shows that the VMD algorithm has better resolution and higher fault diagnosis accuracy after optimizing the parameters.(2)Set the optimal initial parameters of the BP neural network by the global and local optimization iterative ability of improved fish swarm algorithm,it can improve the convergence speed of the neural network,prevent it from falling into local extreme values,and expand its global characteristics.According to this feature,A bearing fault identification method based on improved fish swarm algorithm(ADAFSA)to optimize BP neural network is designed.This method has higher fault recognition rate than the traditional method by analyzing the data.(3)This paper has developed a web-based CNC machine tools rolling bearing fault diagnosis system that can realize on-line monitoring and diagnosis.The system is mainly divided into three parts.The first part is the acquisition of bearing vibration signal.The main function is to use the data acquisition board,acceleration sensor and constant current adapter to collect diagnostic signals and then store them in the database.The second part is signal analysis and fault diagnosis.The main function is to extract the eigenvalues by performing time-domain and frequency-domain analysis on the original signal,and the fault diagnosis method based on BP neural network optimized by improved fish swarm algorithm is used for fault diagnosis and the diagnosis result is obtained.The last part is the Web site and the communication transmission.The main function is to implement the network communication of each part.The diagnostic analysis results are transmitted to the server and stored in the database.The operation status of the machine tool bearings can be viewed at any time and anywhere through the Web site which can achieve user login and permission allocation,login settings and other functionsThe application of this system not only enables users to increase machine tool downtime and reduce maintenance cost,but also enables machine tool manufacturers to improve the quality of after-sales service and reduce expenditures,which has high economic and application value.