基于S时频熵的球轴承性能退化特征指标提取方法
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摘要
滚动轴承的性能退化评估是实现主动维护的关键技术,其对特征提取提出了完全区别于故障模式识别的新要求。作为旋转机械中重要的零件,轴承的振动信号往往具有非平稳性,采用有效的时频分析能充分挖掘蕴含其中的故障信息。S变换是一种兼具小波和短时Fourier变换各自优势的时频分析方法,信息熵则能够定量度量信号分布的复杂程度,因此提出了S时频熵指标用来度量轴承振动信号的复杂度并反映其退化过程。对滚动轴承加速疲劳试验寿命周期数据进行分析并与有效值进行对比表明,S时频熵能够有效反映轴承性能的退化过程,可以作为性能退化评估框下现有指标的有益补充。
The performance degradation assessment for rolling bearings is a key technology to realize proactive maintenance,and a new requirement for feature extraction is proposed that completely different from fault pattern recognition.As one of the important parts in rotating machinery,the vibration signals produced by rolling bearings are nonstationary. The fault information is excavated fully by effective time frequency analysis. The S-transform is a time frequency analysis method,which combines the advantages of wavelet transform and short time Fourier transform. The distribution complexity of signals is measured quantitatively by information entropy. Therefore,the S-time-frequency entropy is used to measure complexity of vibration signals in rolling bearings and reflect its degradation process. The rolling bearing acceleration fatigue life cycle data is analyzed and compared with effective values,the results show that as a useful supplement of existing indicators under frame of performance degradation assessment,the S-time-frequency entropy is able to reflect performance degradation process of bearings effectively.
The performance degradation assessment for rolling bearings is a key technology to realize proactive maintenance,and a new requirement for feature extraction is proposed that completely different from fault pattern recognition.As one of the important parts in rotating machinery,the vibration signals produced by rolling bearings are nonstationary. The fault information is excavated fully by effective time frequency analysis. The S-transform is a time frequency analysis method,which combines the advantages of wavelet transform and short time Fourier transform. The distribution complexity of signals is measured quantitatively by information entropy. Therefore,the S-time-frequency entropy is used to measure complexity of vibration signals in rolling bearings and reflect its degradation process. The rolling bearing acceleration fatigue life cycle data is analyzed and compared with effective values,the results show that as a useful supplement of existing indicators under frame of performance degradation assessment,the S-time-frequency entropy is able to reflect performance degradation process of bearings effectively.
引文
[1] HUANG Y X,LIU C L,ZHA X F,et al. A lean model for performance assessment of machinery using second generation wavelet packet transform and Fisher criterion[J]. Expert Systems with Applications,2010,37(5):3815-3822.
[2] YU J B. Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models[J]. Mechanical Systems and Signal Processing,2011,25(7):2573-2588.
[3]陈小冬,熊国良,张磊.基于S变换和NMF的轴承故障诊断方法[J].华东交通大学学报,2016,33(5):58-64.
[4]李战明,韩阳,韦哲,等.基于S变换的心音信号特征提取[J].振动与冲击,2012,31(21):179-183.
[5]郭远晶,魏燕定,周晓军,等. S变换用于滚动轴承故障信号冲击特征提取[J].振动、测试与诊断,2014,34(5):818-972.
[6]张云鹏,盖强. S变换在滚动轴承故障诊断上的应用[J].应用科技,2011,38(7):26-28.
[7]林龙,郑澜.基于改进广义S变换和2DLDA的轴承故障诊断方法[J].公路与汽运,2017(5):9-13.
[8]潘玉娜.滚动轴承的性能退化特征提取及评估方法研究[D].上海:上海交通大学,2011.
[9] STOCKWELL R G,MANSINHA L,LOWE R P. Localization of the complex spectrum:the S transform[J].IEEE Transactions on Signal Processing,2002,44(4):998-1001.
[10] STOCKWELL R G. Why use the S-transform?[J].Fields Institute Communication,2007,52:279-309.
[2] YU J B. Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models[J]. Mechanical Systems and Signal Processing,2011,25(7):2573-2588.
[3]陈小冬,熊国良,张磊.基于S变换和NMF的轴承故障诊断方法[J].华东交通大学学报,2016,33(5):58-64.
[4]李战明,韩阳,韦哲,等.基于S变换的心音信号特征提取[J].振动与冲击,2012,31(21):179-183.
[5]郭远晶,魏燕定,周晓军,等. S变换用于滚动轴承故障信号冲击特征提取[J].振动、测试与诊断,2014,34(5):818-972.
[6]张云鹏,盖强. S变换在滚动轴承故障诊断上的应用[J].应用科技,2011,38(7):26-28.
[7]林龙,郑澜.基于改进广义S变换和2DLDA的轴承故障诊断方法[J].公路与汽运,2017(5):9-13.
[8]潘玉娜.滚动轴承的性能退化特征提取及评估方法研究[D].上海:上海交通大学,2011.
[9] STOCKWELL R G,MANSINHA L,LOWE R P. Localization of the complex spectrum:the S transform[J].IEEE Transactions on Signal Processing,2002,44(4):998-1001.
[10] STOCKWELL R G. Why use the S-transform?[J].Fields Institute Communication,2007,52:279-309.