基于瞬时包络尺度谱熵的滚动轴承早期故障奇异点识别及特征提取
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摘要
滚动轴承故障信号具有较强的非平稳特性,并且极易受齿轮等噪源污染,故障特征信息微弱,特别当滚动轴承处于故障早期,上述问题尤为严重。针对这一难点,提出基于瞬时包络尺度谱熵的滚动轴承早期故障奇异点识别及特征提取方法。应用重分配尺度谱对轴承的包络信号进行时频分解,计算每一时刻的功率谱熵,以获取信号的瞬时包络尺度谱熵(Instantaneous envelope scalogram entropy,IESE),则信号IESE曲线发生畸变的位置,即是轴承故障表征最为明显的时刻,进而可以提取轴承故障信号的最优故障表征时段(Optimal fault characterization phase,OFCP),应用包络解调和包络尺度谱分析OFCP,以提取轴承故障特征频率。实测信号分析结果表明,该方法能有效提取轴承故障早期的微弱故障特征信息。
The non-stationary characteristics of the bearing fault signal is strong,and the signal is easy to be contaminated by the gear and other noise sources,the fault feature information is weak,especially when the rolling bearing is at the early fault stage,the above problem is particularly serious.To solve the problem above,a method of singular point recognition and feature extraction for the incipient bearing fault is presented based on instantaneous envelope scalogram entropy(IESE).The envelope signal is decomposed by reassigned wavelet scalogram,and the power spectrum entropy of each moment is calculated to obtain the instantaneous envelope scalogram entropy of the signal.The distortion position of the IESE curve of the signal is the most obvious time of bearing fault characterization.And then the optimal fault characterization phase(OFCP) of bearing fault signal could be extracted,and the OFCP could be used to extract the characteristic frequency of bearing fault.The results showed that the method could effectively extract the weak fault feature information of the signal of early fault bearing.1
The non-stationary characteristics of the bearing fault signal is strong,and the signal is easy to be contaminated by the gear and other noise sources,the fault feature information is weak,especially when the rolling bearing is at the early fault stage,the above problem is particularly serious.To solve the problem above,a method of singular point recognition and feature extraction for the incipient bearing fault is presented based on instantaneous envelope scalogram entropy(IESE).The envelope signal is decomposed by reassigned wavelet scalogram,and the power spectrum entropy of each moment is calculated to obtain the instantaneous envelope scalogram entropy of the signal.The distortion position of the IESE curve of the signal is the most obvious time of bearing fault characterization.And then the optimal fault characterization phase(OFCP) of bearing fault signal could be extracted,and the OFCP could be used to extract the characteristic frequency of bearing fault.The results showed that the method could effectively extract the weak fault feature information of the signal of early fault bearing.1
引文
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[10]张顶成,于德介,李星.滚动轴承故障诊断的品质因子可调小波重构方法[J].航空动力学报,2015,30(12):3051-3057.ZHANG Dingcheng,YU Dejie,LI Xing.Fault diagnosis of rolling bearings based on the tunable-Q wavelet reconstruction[J].Journal of Aerospace Power,2015,30(12):3051-3057.
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[12]JIANG R L,CHEN J,DONG G M,et al.The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum[J].Engineering Science Engineers,Part C:Journal of Mechanical Engineering Science,2013,227(5):1116-1129.
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[14]白斌,白广忱,李超.过程功率谱熵在转子振动定量诊断中的应用[J].航空发动机,2015,41(1):27-31.BAI Bin,BAI Guangchen,LI Chao.Application of process power spectrum entropy in rotor vibration quantitative diagnosis[J].Aeroengine,2015,41(1):27-31.
[15]HE Y Y,LIU Y.Experimental research into time-frequency characteristics of cavitation noise using wavelet scalogram[J].Applied Acoustics,2011,72:721-731.
[16]HE Y,YIN X,CHU F.Modal analysis of rubbing acoustic emission for rotor-bearing system based on reassigned wavelet scalogram[J].Journal of Vibration and Acoustics,2008,130(6):061009.
[17]汤宝平,蒋永华,董绍江.重分配小波尺度谱的时频分布优化方法研究[J].仪器仪表学报,2010,31(6):1330-1334.TANG Baoping,JIANG Yonghua,DONG Shaojiang.Time-frequency representation optimization of reassigned wavelet scalogram[J].Chinese Journal of Scientific Instrument,2010,31(6):1330-1334.
[2]刘中磊,于德介,刘坚.基于故障特征频率的阶比双谱方法及其在滚动轴承故障诊断中的应用[J].中国电机工程学报,2013,33(33):123-129.LIU Zhonglei,YU Dejie,LIU Jian.Order bispectrum analysis based on fault characteristic frequency and its application to the fault diagnosis of rolling bearings[J].Proceedings of the CSEE,2013,33(33):123-129.
[3]胥永刚,孟志鹏,陆明,等.双树复小波和奇异差分谱在滚动轴承故障诊断中的应用[J].振动工程学报,2013,26(6):965-973.XU Yonggang,MENG Zhipeng,LU Ming,et al.Application of dual tree complex wavelet transform and singular value difference spectrum in the rolling bearing fault diagnosis[J].Journal of Vibration Engineering,2013,26(6):965-973.
[4]雷亚国,韩冬,林京,等.自适应随机共振新方法及其在故障诊断过中的应用[J].机械工程学报,2012,48(7):62-67.LEI Yaguo,HAN Dong,LIN Jing,et al.New adaptive stochastic resonance method and its application to fault diagnosis[J].Journal of Mechanical Engineering,2012,48(7):62-67.
[5]WANG T Y,LIANG M,LI J Y.Rolling element bearing fault diagnosis via fault characteristic order(FCO)analysis[J].Mechanical Systems and Signal Processing.2014,45(1):139-153.
[6]王天杨,李建勇,程卫东.基于低次故障特征阶比系数的变转速滚动轴承等效转频估计算法[J].机械工程学报,2015,51(3):121-128.WANG Tianyang,LI Jianyong,CHENG Weidong.Equivalent rotational frequency estimation algorithm of faulty rolling bearing under varying rotational speed based on the lower fault characteristic order coefficient[J].Journal of Mechanical Engineering,2015,51(3):121-128.
[7]段晨东,高鹏,徐先峰,等.一种基于时频峭度谱的滚动轴承损伤诊断方法[J].机械工程学报,2015,51(11):78-83.DUAN Chendong,GAO Peng,XU Xianfeng,et al.A ball bearing defect diagnosis method using time-frequency Kurtosis spectrum[J].Journal of Mechanical Engineering,2015,51(11):78-83.
[8]李川,朱荣荣,杨帅.基于多指标模糊融合的滚动轴承诊断的最优频带解调方法[J].机械工程学报,2015,51(7):107-114.LI Chuan,ZHU Rongrong,YANG Shuai.Optimal frequency band demodulation for fault diagnosis of rolling element bearings based on fuzzy fusion of multiple criteria[J].Journal of Mechanical Engineering,2015,51(7):107-114.
[9]WANG D,TSE P W,TSUI K L.An enhanced Kurtogram method for fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2013,35:176-199.
[10]张顶成,于德介,李星.滚动轴承故障诊断的品质因子可调小波重构方法[J].航空动力学报,2015,30(12):3051-3057.ZHANG Dingcheng,YU Dejie,LI Xing.Fault diagnosis of rolling bearings based on the tunable-Q wavelet reconstruction[J].Journal of Aerospace Power,2015,30(12):3051-3057.
[11]唐贵基,王晓龙.自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J].中国电机工程学报,2015,35(6):1436-1444.TANG Guiji,WANG Xiaolong.Adaptive maximum correlated Kurtosis deconvolution method and its application on incipient fault diagnosis of bearing[J].Proceedings of the CSEE,2015,35(6):1436-1444.
[12]JIANG R L,CHEN J,DONG G M,et al.The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum[J].Engineering Science Engineers,Part C:Journal of Mechanical Engineering Science,2013,227(5):1116-1129.
[13]申弢,黄树红,韩守木,等.旋转机械振动信号的信息熵特征[J].机械工程学报,2004,37(6):94-98.SHEN Tao,HUANG Shuhong,HAN Shoumu,et al.Extracting information entropy features for rotating machinery vibration signals[J].Chinese Journal of Mechanical Engineering,2004,37(6):94-98.
[14]白斌,白广忱,李超.过程功率谱熵在转子振动定量诊断中的应用[J].航空发动机,2015,41(1):27-31.BAI Bin,BAI Guangchen,LI Chao.Application of process power spectrum entropy in rotor vibration quantitative diagnosis[J].Aeroengine,2015,41(1):27-31.
[15]HE Y Y,LIU Y.Experimental research into time-frequency characteristics of cavitation noise using wavelet scalogram[J].Applied Acoustics,2011,72:721-731.
[16]HE Y,YIN X,CHU F.Modal analysis of rubbing acoustic emission for rotor-bearing system based on reassigned wavelet scalogram[J].Journal of Vibration and Acoustics,2008,130(6):061009.
[17]汤宝平,蒋永华,董绍江.重分配小波尺度谱的时频分布优化方法研究[J].仪器仪表学报,2010,31(6):1330-1334.TANG Baoping,JIANG Yonghua,DONG Shaojiang.Time-frequency representation optimization of reassigned wavelet scalogram[J].Chinese Journal of Scientific Instrument,2010,31(6):1330-1334.