基于EEMD与CPSO-ELM的车削机床刀具磨损故障检测与识别

鉴于数控车床刀具在机械加工系统中占有重要的地位,故数控车床刀具磨损故障的在线检测与识别具有重要意义。以华中数控车床为研究对象,提出了以平均经验模态分解(EEMD)、混沌粒子群(CPSO)以及核极限学习机(ELM)等方法对车床刀具磨损故障进行诊断。介绍了EEMD、CPSO以及ELM的基本原理和过程;对采集得到的刀具磨损信号进行前期预处理,经EEMD分解后得到IMF分量,以峭度、峰值、均方根值作为一种选取标准,选择包含较多故障信息的几个IMF进行信号重组并计算;将计算结果组成特征向量输入CPSO-ELM、SVM以及BP神经网络等分类器进行故障识别和对比。实验结果表明:对比传统的BP神经网络和SVM分类器,CPSO-ELM分类器具有快速、精确、有效的识别特性,能够有效检测和识别刀具磨损故障。

Wear Fault Detection and Identification of Turning Machine Tools Based on EEMD and CPSO-ELM

In view of the important position of CNC lathe cutter in machining system, on-line detection and recognition of tool wear fault of CNC lathe are of great significance. Taking the Central China CNC lathe as the research object,the methods of means of average empirical modal decomposition (EEMD), chaotic particle swarm optimization (CPSO) and nuclear limit learning machine (ELM) were proposed to diagnose the lathe tool wear fault.The basic principles and processes of EEMD, CPSO and ELM were introduced. The acquired tool wear signals were pre-processed, and the IMF component was obtained after EEMD decomposition.Taking the Kurtosis, peak value and root-mean-square value as a selection criterion,several IMF contained more fault information were selected for signal recombination and calculation.After composing the feature vectors,the calculation results were input into CPSO-ELM, SVM and BP neural network and other classifiers for fault identification and comparison. The experimental results show that compared with the traditional BP neural network and SVM classifier, the CPSO-ELM classifier has the characteristics of fast, accurate and effective identification, and can effectively detect and identify tool wear faults.