基于多尺度形态学分解谱熵的电机轴承性能退化特征提取
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摘要
结合数学形态学与信息熵理论,提出一种基于多尺度形态分解谱熵的电机轴承退化特征提取方法。对不同损伤程度轴承的振动信号进行多尺度形态分解,分别计算其在不同尺度域内的复杂性度量:能谱熵与奇异谱熵,以其作为预测特征矢量可以对性能退化趋势有很好的线性反映能力。仿真与实例数据验证了此方法的有效性。
Combining the mathematical morphology with information entropy theory,a method is proposed for degeneration feature extraction of motor bearings based on multi scale morphological decomposition spectrum entropy.The multi scale morphological decomposition is carried out for bearing vibration signals with different damage degree,the complexity indicator of power spectral entropy and singular spectral entropy in different scale domain is computed separately.Taking the two indicators as forecasting character vector is able to linearly reflect performance degeneration trend.The effectiveness of the method is verified by simulation and instance data.
Combining the mathematical morphology with information entropy theory,a method is proposed for degeneration feature extraction of motor bearings based on multi scale morphological decomposition spectrum entropy.The multi scale morphological decomposition is carried out for bearing vibration signals with different damage degree,the complexity indicator of power spectral entropy and singular spectral entropy in different scale domain is computed separately.Taking the two indicators as forecasting character vector is able to linearly reflect performance degeneration trend.The effectiveness of the method is verified by simulation and instance data.
引文
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[22]徐东,徐永成,陈循,等.基于EMD的灰色模型的疲劳剩余寿命预测方法研究[J].振动工程学报,2011,24(1):104-110.
[23]Kenneth A Loparo.Bearing Data Center[EB/OL].CaseWestern Reserve University,2000[2010-03-26].http:∥www.eecs.case.edu/laboratory/bearing/wel-come_overview.htm.
[2]Qiu H,Lee J,Lin J,et al.Robust Performance Degra-dation Assessment Methods for Enhanced Rolling Ele-ment Bearing Prognostics[J].Advanced EngineeringInformatics,2003,17:127-140.
[3]Zhang X D,Xu R,Kwan C,et al.An Integrated Ap-proach to Bearing Fault Diagnostics and Prognostics[C].2005 American Control Conference,2005:2 750-2 755.
[4]曾庆虎,邱静,刘冠军.基于小波相关特征尺度熵的HSMM设备退化状态识别与故障预测方法[J].仪器仪表学报,2008,12(29):2559-2564.
[5]曾庆虎,邱静,刘冠军.基于HSMM的机械故障演化规律分析建模与预测[J].机械强度,2010,32(5):695-701.
[6]肖文斌,陈进,周宇,等.小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用[J].振动与冲击,2011,30(8):32-35.
[7]潘玉娜,陈进.小波包-支持向量数据描述在轴承性能退化评估中的应用研究[J].振动与冲击,2009,28(4):164-167.
[8]潘玉娜,陈进.结合循环平稳和支持向量数据描述的轴承性能退化评估研究[J].机械科学与技术,2009,28(4):442-445.
[9]Serra J.Morphological Filtering:Overview[J].SignalProcessing,1994,38(4):3-11.
[10]Nikolaou N G,Antoniadis I A.Application of Morpho-logical Operators as Envelope Extractors for Impul-sive-Type Periodic Signals[J].Mechanical Systemsand Signal Processing,2003,17(6):1 147-1 162.
[11]束洪春,王晶,陈学允.动态电能质量扰动的多刻度形态学分析[J].中国电机工程学报,2004,24(4),63-67.
[12]王冰,李洪儒,许葆华.一种多元素多尺度形态非抽样小波分解方法[J].轴承,2013(5):43-48.
[13]Wang D.Classification of Remotely Sensed Images U-sing Mathematical Morphology[C]∥Proceedings ofIGARSS,1994:1 615-1 617.
[14]段中钰,王润秋.多尺度形态学在地震资料处理中的应用研究[J].勘探地球物理进展,2010,33(2):135-140.
[15]赵军,吕艳萍,王汉广.基于多尺度形态分解的特高压线路雷击干扰识别新方法[J].高电压技术,2009,35(5):994-997.
[16]胡爱军,唐贵基,安连锁.基于数学形态学的旋转机械振动信号降噪方法[J].机械工程学报,2006,42(4):127-130.
[17]沈路,周晓军,张文斌,等,广义数学形态滤波器的旋转机械振动信号降噪[J].振动与冲击,2009,28(9):70-73.
[18]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.
[19]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,2001,37(6):94-98.
[20]苗刚,马孝江,任全民.多尺度Hilbert谱熵在故障诊断中的应用[J].农业机械学报,2007,38(3):176-178.
[21]Vautard R,Yiou P,Ghil M.Singular-Spectrum Anal-ysis:A Toolkit for Short,Noisy Chaotic Signals[J].Physica D:Nonlinear Phenomena,1992,58(1-4):95-126.
[22]徐东,徐永成,陈循,等.基于EMD的灰色模型的疲劳剩余寿命预测方法研究[J].振动工程学报,2011,24(1):104-110.
[23]Kenneth A Loparo.Bearing Data Center[EB/OL].CaseWestern Reserve University,2000[2010-03-26].http:∥www.eecs.case.edu/laboratory/bearing/wel-come_overview.htm.
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