Research On Fault Diagnosis System Of Electrical Spindle In Machining Center

Machining center is an automatic tool change CNC machine tool that has been rapidly developed to meet the requirements of time saving,labor saving and energy saving.The high speed electric spindle unit is the key part to determine the high speed and high precision of CNC machine tools,and it is always the basis for the development of machine tool technology.The performance of the spindle components will directly affect the quality of the machining center's products.During the actual use process,the spindle may be subject to shock and vibration due to incorrect program operation,improper machine setup,and an operator's non-standard operation.For enterprises and users,it is hoped that CNC machine tools can operate in an economical,safe,and efficient manner.Therefore,it is of great significance to monitor and diagnosis the production process of the machining center spindle.From the perspective of improving the performance of the machining center's electric spindle and real-time control the operating conditions of the key components.In this dissertation,the electric spindle's bearing is used as the object to carry out the algorithm research of the fault diagnosis system,and the feature extraction methods such as time domain features,frequency domain features and time-frequency analysis are verified.And then establish a neural network fault diagnosis model.At last,the dissertation takes the horizontal machining center DMC65 H of Germany as the research object and completes the compiling of the fault diagnosis system of the machining center spindle.The main research contents of the dissertation are as follows:(1)The dissertation introduced the source and the research background and significance of the project.And then discussed the current status of monitoring and fault diagnosis technologies at home and abroad.After making a thorough study on the structural characteristics,working principle,fault types and causes of machining spindle,the fault diagnosis system was developed.(2)The dissertation described the key technologies such as data acquisition and storage,noise reduction,feature extraction,online monitoring and fault diagnosis,and selected the hardware devices,analyzed the requirements and development principles of software systems,provided theoretical basis and technical support for later software development.(3)After researched the requirements of the market and users,and investigated the site of the machining center,the dissertation using a graphical programming language LabVIEW software developed a machining center online spindle monitoring and fault diagnosis system.The overall structure of the system mainly includes data acquisition,data processing,data analysis,data storage and fault diagnosis modules.Among them,feature extraction and fault diagnosis are the key points of the entire software system.This dissertation used common time-frequency domain analysis,used time domain characteristic parameters and EEMD's IMF component frequency domain characteristic parameters for fault feature extraction,and then used BP neural network diagnostic model for fault diagnosis to determines the type of spindle fault.With the expectation that the software can timely and correctly diagnose the operating conditions of the operating status of the machining center spindle,and timely feedback the operation status to the operator,prevent failures in advance,and improve the reliability and safety of the machine tool operation.Finally,through the experimental data for various functional analysis,and after on-site testing and debugging,the fault diagnosis system functions is normal,basically achieved the design purpose.