摘要数控机床刀具磨损监测对于提高数控机床利用率，减小由于刀具破损而造成的经济损失具有重要意义。有针对性地回顾了国内外各种分析刀具磨损信号方法的讨论工作，详细叙述了功率谱分析法、小波变换、人工神经网络以及多传感器信息融合技术的实现形式。通过比较各种数据处理方法的优缺点，提出基于混合智能多传感器信息融合技术是数控机床刀具磨损监测实验数据处理的未来进展的主要方向。以数控机床刀具故障诊断系统构建与测试方法讨论为目标，进行了刀具磨损实验。采纳振动传感器、声发射传感器对切削过程中不同磨损程度刀具的信号进行检测、分析和故障诊断。以 LabVIEW10.0 为开发平台，开发了一套包括数据采集模块、信号分析模块、故障诊断模块的数控机床故障诊断实验系统。关键词：数控机床；刀具磨损监测；数据处理;LabVIEW; 声发射; 振动AbstractCNC tool wear monitoring would be a great significance for improving the usage rate of CNC and reducing the economic losses due to the tool breakage. The recent research progress on the signal analyzing was reviewed. Some important data process methods were detailed described，such as power spectrum analysis，wavelet transform，artificial neural network and intelligent sensor fusion technology. By comparing their features，the intelligent sensor fusion technology was introduced to be popular in data processing method for CNC tool wear monitoring. To build fault diagnosis system of CNC machine tools and get test method，tool wear experiments are carried out． Signals for cutting tool with different wearing degrees in milling process are detected and analyzed through vibration sensors and acoustic emission sensors on the milling tools ． Using LabVIEW8 ． 6 as development platform ， a fault diagnosis experimental system of CNC machine tools is developed，including data acquisition module，signal analysis module and fault diagnosis moduleKey words：CNC；Tool wear monitoring；Data processing method; LabVIEW; vibration目录...
