机械故障诊断技术中的信号处理方法:时频分析
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摘要
当机械设备的振动信号为非平稳信号和时变信号这类特殊信号时,时域分析和频域分析因其自身的局限,无法取得很好的分析效果,需要使用时间和频率的联合函数来表示信号,即信号的时频表示。针对常用的时频分析振动信号处理方法,总结多种算法的特征和优缺点。根据常见机械设备关键构件的振动特征,选择不同的信号处理和特征提取算法进行分析,以便提高多种构件、多类故障的特征提取精度和可靠性,从而为有效地实现机械设备的故障提供参考和指导。
When vibration signals of mechanical equipment are non-stationary or time-varying,neither time domain analysis nor frequency-domain analysis can provide satisfactory results.In contrast,the time-frequency analysis can signify the signals well.In this paper,the characteristics,advantage and disadvantage of several time-frequency analysis methods were summarized for the purpose of machinery fault diagnosis.According to the vibration characteristics of the key components of some popular mechanical equipment,some signal processing methods and feature extraction algorithms were selected and analyzed in order to improve the precision and reliability for multi-fault diagnosis of the components.
When vibration signals of mechanical equipment are non-stationary or time-varying,neither time domain analysis nor frequency-domain analysis can provide satisfactory results.In contrast,the time-frequency analysis can signify the signals well.In this paper,the characteristics,advantage and disadvantage of several time-frequency analysis methods were summarized for the purpose of machinery fault diagnosis.According to the vibration characteristics of the key components of some popular mechanical equipment,some signal processing methods and feature extraction algorithms were selected and analyzed in order to improve the precision and reliability for multi-fault diagnosis of the components.
引文
[1]赵淑红.短时傅立叶变换与Wigner-Ville分布联合确定地震信号瞬时频率[J].西安科技大学学报,2010,4:447-450.
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[4]李飞行,等.时频分析在发动机振动监测与故障诊断中的应用[J].测控技术,2011,2:19-22.
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[22]Loutridis S.,Douka E.,et al.Forced vibration behaviorand crack detection of cracked beams usinginstantaneous frequency[J].NDT&EInternational,2005,38(3):411-419.
[2]科恩著,白居宪译.时频分析:理论与应用[M].西安:西安交通大学出版社,1998.
[3]Yunxin Zhao,Les E.Atlas.The use of cone-shaped kernelsfor generalized time-frequency representation ofnonstationary signals[J].IEEE Transactions OnAcoustics,Speech and Signal Processing,2001,38:1084-1091.
[4]李飞行,等.时频分析在发动机振动监测与故障诊断中的应用[J].测控技术,2011,2:19-22.
[5]吴子英,等.短时傅立叶变换在大型水轮发电机组振动分析中的应用[J].水力发电学报,2005,6:115-120.
[6]金瑜.基于小波神经网络的模拟电路故障诊断方法研究[D].电子科技大学,2008.
[7]翟彦.小波分析理论在信号处理中的研究及应用[D].昆明:昆明理工大学,2011.
[8]于琳.LabVIEW平台下基于小波包神经网络的浊度传感器故障诊断[D].广州:广东工业大学,2009.
[9]张丰功.小波分析在机械故障诊断中的应用[J].重型机械科技,2007,1:42-44.
[10]李桃.基于信号处理的齿轮箱故障诊断研究[J].机械管理开发,2011,4:208-211.
[11]何正嘉,李富才,杜远,陈鹏.小波技术在机械监测诊断领域的应用现状与进展[J].西安交通大学学报,2001,5:540-545.
[12]刘正平,王彦强.基于小波分析的滚动轴承故障诊断[J].煤矿机械,2011,8:266-268.
[13]成礼智.小波变换图像压缩中最优小波基的选取方法[J].数字电视与数字视频,2004,10:4-7.
[14]赵婷.基于小波分析的机械振动故障分析[D].沈阳:沈阳工业大学,2008.
[15]吴勇.基于小波分析的高速牵引电机轴承故障诊断研究[D].北京:北京交通大学,2011.
[16]高强,等.一种改进的经验模式分解方法[J].振动与冲击,2010,8:151-153.
[17]张宾,等.基于经验模式分解的拖曳式声纳拖船噪声抵消研究[J].应用声学,2007,2:68-72.
[18]陈卫萍,等.基于经验模态分解的小波阈值滤波去噪[J].安徽工业大学学报,2010,4:397-399.
[19]徐敏.设备故障诊断手册[M].西安:西安交通大学出版社,1998.
[20]Huang N E,Zheng Shen,R Long Steven,et al.Theempirical mode decomposition and the hilbert spectrumfor non-linear and non-stationary time series analysis[J].Proceedings of the Royal-society of London,1998,454A:903-995.
[21]DarryII Pinesa,Liming Salvinob.Structural healthmonitoring using empirical mode decomposition and theHilbert phase[J].Journal of Sound and Vibration,2006,294(6):97-124.
[22]Loutridis S.,Douka E.,et al.Forced vibration behaviorand crack detection of cracked beams usinginstantaneous frequency[J].NDT&EInternational,2005,38(3):411-419.
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