基于LCD多尺度散布熵的数控机床故障诊断
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摘要
为提升数控机床用轴承故障诊断效果,提出了基于LCD多尺度散布熵的数控机床轴承故障诊断方法。该方法利用局部特征尺度分解(LCD)对轴承振动信号进行分解,获取原始信号不同尺度下的内禀尺度分量(ISC);根据散布熵能有效区分不同故障信号的复杂度,计算ISC分量的散布熵,获得原始信号多个尺度下的复杂度特征作为轴承故障的特征参数;将该特征参数输入SVM分类器中判断轴承故障,实现故障诊断。轴承不同类型和不同程度故障诊断实验结果表明,所提方法能够提升轴承的故障诊断效果,相比其他一些方法,具有一定的优势。
In order to improve fault diagnosis effect of bearing used in NC machine, a fault feature extraction and diagnosis method of bearing based on LCD multi dispersion entropy was proposed. The vibration signal was decomposed adaptively with local characteristic-scale decomposition(LCD) to obtain the components in different scales of the original signal. Considering the ability of the dispersion entropy in distinguishing the complexity of different signals effectively, the dispersion entropy of intrinsic scale components(ISCs) by LCD was calculated. Thus the complexity metric in different scales of the original signal was gained, which was consequently taken as the feature parameter to describe different bearing states. The feature parameters were then put into SVM for diagnosing the bearing faults. Bearing different fault type and different fault degree diagnosis results show that the proposed method can improve diagnosis effect and has certain superiority when compared with some other methods.
In order to improve fault diagnosis effect of bearing used in NC machine, a fault feature extraction and diagnosis method of bearing based on LCD multi dispersion entropy was proposed. The vibration signal was decomposed adaptively with local characteristic-scale decomposition(LCD) to obtain the components in different scales of the original signal. Considering the ability of the dispersion entropy in distinguishing the complexity of different signals effectively, the dispersion entropy of intrinsic scale components(ISCs) by LCD was calculated. Thus the complexity metric in different scales of the original signal was gained, which was consequently taken as the feature parameter to describe different bearing states. The feature parameters were then put into SVM for diagnosing the bearing faults. Bearing different fault type and different fault degree diagnosis results show that the proposed method can improve diagnosis effect and has certain superiority when compared with some other methods.
引文
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[9] 张良,张前图.基于LCD模糊熵和流行学习的故障特征提取方法[J].机械强度,2016,38(2):225-230.ZHANG Liang,ZHANG QianTu.Fault feature extraction method baDEd on LCD fuzzy entropy and manifold learning[J].Journal of Mechanical Strength,2016,38(2):225-230(In Chinese).
[10] 贾峰,武兵,熊晓燕,等.基于多维度排列熵和支持向量机的轴承早期故障诊断方法[J]|.计算机集成制造系统,2014,20(9):2275-2282.JIA Feng,WU Bing,XIONG XiaoYan,et al.Early fault diagnosis of bearing baDEd on multi-dimension permutation entropy and SVM[J].Computer Integrated Manufacturing Systems,2014,20(9):2275-2282(In Chinese).
[11] 许凡,方彦军,张荣,等.基于LMD基本尺度熵的AP聚类滚动轴承故障诊断[J].计算机应用研究,2016,33(7):1-7.XU Fan,FANG Yanjun,ZHANG Rong,etal.Rolling bearing fault diagnosis method based on LMD base-scale entropy and AP clustering[J].Application Research of Computers,2016,33(7):1-7(In Chinese).
[12] 王冰,李洪儒,许葆华.基于多尺度形态分解谱熵的轴承预测特征提取及退化状态评估[J].振动与冲击,2013,32(22):124-128.WANG Bing,LI Hongru,XU Baohua.Motor bearing forecast feature extracting and degradation status identification based on multi-scale morphological decomposition spectral entropy[J].Journal of Vibration and Shock,2013,32(22):124-128(In Chinese).
[13] 张西宁,雷威,李兵.主分量分析和隐马尔科夫模型结合的轴承监测诊断方法[J].西安交通大学学报,2017,51(6):1-8.ZHANG Xiling,LEI Wei,LI Bing.Bearing fault diagnosis method based on principal component analysis and hidden markov model[J].Journal of Xi’an Jiaotong University,2017,51(6):1-8(In Chinese).
[14] 刘吉彪,程军圣,刘燕飞.基于LCD和GMM-VPMCD混合模型的滚动轴承退化状态识别[J].机械强度,2016,38(6):1161-1166.LIU JIbiao,CHENG Junsheng,LIU Yanfei.The degradation state recognition method of rolling bearing based on LCD and GMM-VPMCD hybrid model[J].Journal of Mechanical Strength,2016,38(6):1161-1166(In Chinese).
[15] 魏永合,王明华.基于EEMD和SVM的轴承退化状态识别[J].计算机集成制造系统,2015,21(9):2475-2483.WEI Yonghe,WANG Minghua.Degradation state recognition of rolling bearing based on EEMD and SVM[J].Computer Integrated Manufacturing Systems,2015,21(9):2475-2483(In Chinese).
[2] 李峰,王家序,杨荣松.有监督不相关正交局部保持映射故障辨识[J].仪器仪表学报,2013,34(5):1113-1120.LI Feng,WANG JiaXu,YANG RongSong.Fault identification method based on supervised uncorrelated orthogonal locality preserving projection[J].Chinese Journal of Scientific Instrument,2013,34(5):1113-1120(In Chinese).
[3] 李晨晨,韩清鹏,李天成,等.EEMD和分形组合技术对ECS涡轮轴承故障特征提取的研究[J/OL].机械科学与技术,2018,http://kns.cnki.net/kcms/detail/61.1114.TH.20180524.2252.017.html.LI ChenChen,HAN QingPeng,LI TianCheng,et al.Study on fault feature extractionof ECS turbine bearing by combination of EEMD and correlation dimension[J].Mechanical Science and Technology for Aerospace Engineering,2018,http://kns.cnki.net/kcms/detail/61.1114.TH.20180524.2252.017.html(In Chinese).
[4] 张淑清,孙国秀,李亮,等.基于LMD近似熵和FCM聚类的机械故障诊断研究[J].仪器仪表学报,2013,34(03):714-720.ZHANG ShuQing,SUN GuoXiu,LI Liang,et al.Study on mechanical fault diagnosis method baDEd on LMD approximate entropy and fuzzy C-means clustering[J].Chinese Journal of Scientific Instrument,2013,34(3):714-720(In Chinese).
[5] 向丹,葛爽.基于EMD样本熵-LLTSA的故障特征提取方法[J].航空动力学报,2014,29(07):1535-1542.XIANG Dan,GE Shuang.Method of fault feature extraction baDEd on EMD sample entropy and LLTSA[J].Journal of Aerospace Power,2014,29(7):1535-1542(In Chinese).
[6] MOSTAFA ROSTAGHI,HAMED AZAMI.Dispersion Entropy:A Measure for Time-series Analysis[J].Ieeesignal Processing Letters,2016,23(5):610-614.
[7] 程军圣,郑近德,杨宇.一种新的非平稳信号分析方法—局部特征尺度分解法[J].振动工程学报,2012,25(02):215-220.CHEN JunSheng,ZHEN JinDe,YANG Yu.A nonstationary signal analysis approach—the local characteristic-scale decomposition method[J].Journal of Vibration Engineering,2012,25(2):215-220(In Chinese).
[8] 吴震宇,袁惠群,李玲.基于混沌特征和支持向量机的内燃机故障诊断 [J].机械强度,2010,32(5):723-728.WU Z Y,YUAN H Q,LI L.Fault diagnosis of engine based on chaotic features and support vector machine[J].Journal of Mechanical Strength,2010,32(5):723-728(In Chinese).
[9] 张良,张前图.基于LCD模糊熵和流行学习的故障特征提取方法[J].机械强度,2016,38(2):225-230.ZHANG Liang,ZHANG QianTu.Fault feature extraction method baDEd on LCD fuzzy entropy and manifold learning[J].Journal of Mechanical Strength,2016,38(2):225-230(In Chinese).
[10] 贾峰,武兵,熊晓燕,等.基于多维度排列熵和支持向量机的轴承早期故障诊断方法[J]|.计算机集成制造系统,2014,20(9):2275-2282.JIA Feng,WU Bing,XIONG XiaoYan,et al.Early fault diagnosis of bearing baDEd on multi-dimension permutation entropy and SVM[J].Computer Integrated Manufacturing Systems,2014,20(9):2275-2282(In Chinese).
[11] 许凡,方彦军,张荣,等.基于LMD基本尺度熵的AP聚类滚动轴承故障诊断[J].计算机应用研究,2016,33(7):1-7.XU Fan,FANG Yanjun,ZHANG Rong,etal.Rolling bearing fault diagnosis method based on LMD base-scale entropy and AP clustering[J].Application Research of Computers,2016,33(7):1-7(In Chinese).
[12] 王冰,李洪儒,许葆华.基于多尺度形态分解谱熵的轴承预测特征提取及退化状态评估[J].振动与冲击,2013,32(22):124-128.WANG Bing,LI Hongru,XU Baohua.Motor bearing forecast feature extracting and degradation status identification based on multi-scale morphological decomposition spectral entropy[J].Journal of Vibration and Shock,2013,32(22):124-128(In Chinese).
[13] 张西宁,雷威,李兵.主分量分析和隐马尔科夫模型结合的轴承监测诊断方法[J].西安交通大学学报,2017,51(6):1-8.ZHANG Xiling,LEI Wei,LI Bing.Bearing fault diagnosis method based on principal component analysis and hidden markov model[J].Journal of Xi’an Jiaotong University,2017,51(6):1-8(In Chinese).
[14] 刘吉彪,程军圣,刘燕飞.基于LCD和GMM-VPMCD混合模型的滚动轴承退化状态识别[J].机械强度,2016,38(6):1161-1166.LIU JIbiao,CHENG Junsheng,LIU Yanfei.The degradation state recognition method of rolling bearing based on LCD and GMM-VPMCD hybrid model[J].Journal of Mechanical Strength,2016,38(6):1161-1166(In Chinese).
[15] 魏永合,王明华.基于EEMD和SVM的轴承退化状态识别[J].计算机集成制造系统,2015,21(9):2475-2483.WEI Yonghe,WANG Minghua.Degradation state recognition of rolling bearing based on EEMD and SVM[J].Computer Integrated Manufacturing Systems,2015,21(9):2475-2483(In Chinese).