Performance Evaluation And Predictive Maintenance Of Key Equipment In Engine Case Production Line

Numerical control machining equipment is developing towards the direction of intelligence and information.This paper takes the domestic machine tools of engine gearbox production line as the research object,and carries out the research on the efficiency evaluation and predictive maintenance technology of key equipment of engine gearbox production line.First of all,aiming at the problem that it is impossible to accurately identify the running state of the machine tool in the process of efficiency monitoring of CNC machining equipment,the key node identification method of running state based on the integration of energy consumption and CNC system information and the online acquisition method of energy efficiency are studied,and the processing efficiency and energy consumption monitoring data module of the equipment are designed and developed,which is arranged in the state monitoring system of the gearbox production line of Harbin Dong'an company In the monitoring system,the efficiency monitoring and evaluation of production line equipment are realized.Secondly,a fault diagnosis method based on Gaussian random mapping and extreme learning machine is proposed.Aiming at the problem of low efficiency of model operation caused by multi-source information fusion technology in fault diagnosis,this paper proposes a fault diagnosis method for CNC machine tools based on Gaussian random mapping and extreme learning machine.Compared with the traditional dimensionality reduction method,the random mapping method has superior timeliness and high learning rate of extreme learning machine,which makes it possible to realize the rapid diagnosis and location of various types of machine tool faults,and has important engineering practical value.Finally,the predictive maintenance technology of NC machining equipment is studied.According to the different fault types of NC machining equipment,according to the implementation process of predictive maintenance,two types of fault failure forms and their corresponding predictive maintenance schemes are defined.Aiming at the most common types of tool wear fault in NC machining equipment,a tool wear state prediction and recognition method based on lmd-pe and extreme learning machine is proposed.Finally,a remote maintenance module based on fault diagnosis results and state prediction model is developed to improve the function of the system.