旋转机械损伤跟踪的改进相空间曲变法
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摘要
对旋转机械系统损伤演化过程的实时监测和跟踪,能够有效降低设备使用风险以及减少不必要的维护成本,提高设备使用效能。基于相空间曲变理论和旋转机械运行具有周期性的特点,提出了一种改进的损伤跟踪方法,可以有效减少原相空间曲变法的计算量和跟踪误差,实现系统损伤演化过程的实时跟踪。通过数值模拟方法,以及裂纹转子实验和轴承退化实验验证了该方法。结果表明:该方法相比于原相空间曲变法计算时间明显减小,跟踪结果更加平滑准确,对相近的损伤状态有更好的分辨能力。
Monitoring in real time and tracking damage evolution of rotating mechanical systems can effectively reduce their operation risk and unnecessary maintenance costs to improve equipment efficiency. Here,based on the phase space warping theory and the periodic characteristic of rotating machineries,a modified damage-tracking method was proposed to reduce computation cost and tracking error of the original phase space warping method,and realize tracking the system's damage evolution in real time. Numerical simulations and tests of cracked rotors and bearing degradation verified the effectiveness of the proposed method. The results showed that compared to the original phase space warping method,the computation time of the proposed method obviously decreases,the tracking result is more smooth and accurate,and the proposed method has a better distinguish ability for similar damage states.
Monitoring in real time and tracking damage evolution of rotating mechanical systems can effectively reduce their operation risk and unnecessary maintenance costs to improve equipment efficiency. Here,based on the phase space warping theory and the periodic characteristic of rotating machineries,a modified damage-tracking method was proposed to reduce computation cost and tracking error of the original phase space warping method,and realize tracking the system's damage evolution in real time. Numerical simulations and tests of cracked rotors and bearing degradation verified the effectiveness of the proposed method. The results showed that compared to the original phase space warping method,the computation time of the proposed method obviously decreases,the tracking result is more smooth and accurate,and the proposed method has a better distinguish ability for similar damage states.
引文
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[20]JIANG H,CHEN J,DONG G,et al.Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis[J].Mechanical Systems&Signal Processing,2015(1):338-359.
[21]申中杰,陈雪峰,何正嘉,等.基于相对特征和多变量支持向量机的滚动轴承剩余寿命预测[J].机械工程学报,2013,49(2):183-189.SHEN Zhongjie,CHEN Xuefeng,HE Zhengjia,et al.Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine[J].Journal of Mechanical Engineering,2013,49(2):183-189.
[2]TORKAMANI S,BUTCHER E A,TODD M D,et al.Detection of system changes due to damage using a tuned hyper chaotic probe[J].Smart Materials and Structures,2011,20(2):25006-25016.
[3]CHELIDZE D,CUSUMANO J P,CHATTERJEE A.Adynamical systems approach to damage evolution tracking,part 1:description and experimental application[J].Journal of Vibration and Acoustics,2002,124(2):250-257.
[4]CHELIDZE D,CUSUMANO J P.Phase space warping:nonlinear time-series analysis for slowly drifting systems[J].Philosophical Transactions Mathematical Physical&Engineering Sciences,2006,364(1846):2495-2513.
[5]胡雷,范彬,胡茑庆.轴承故障跟踪预测的相空间曲变方法[J].振动与冲击,2012(增刊1):404-407.HU Lei,FAN bin,HU Niaoqing.Application of phase space warping on damage tracking for bearing fault[J].Journal of Vibration and Shock,2012(Sup 1):404-407.
[6]范彬,胡雷,胡茑庆.变工况下旋转机械故障跟踪的相空间曲变方法[J].物理学报,2013,62(16):55-62.FAN bin,HU lei,HU Niaoqing.Fault tracking of rotating machinery under variable operation based on phase space warping[J].Acta Physica Sinica,2013,62(16):55-62.
[7]QIAN Y,YAN R,GAO R X.A multi-time scale approach to remaining useful life prediction in rolling bearing[J].Mechanical Systems&Signal Processing,2016,83:549-567.
[8]SEGALA D B,GATES D H,DINGWELL J B,et al.Nonlinear smooth orthogonal decomposition of kinematic features of sawing reconstructs muscle fatigue evolution as indicated by electromyography.[J].Journal of Biomechanical Engineering,2011,133(3):031009.
[9]LIU M,CHELIDZE D.Identifying damage using local flow variation method[J],Smart Materials&Structures,2006,15:1830-1836.
[10]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings Mathematical Physical&Engineering Sciences,1998,454(1971):903-995.
[11]SAUER T,YORKE J A,CASDAGLI M.Embedology[J].Journal of Statistical Physics,1991,65(3/4):579-616.
[12]KENNEL M,BROWN R,ABARBANEL H.Determining embedding dimension for phase-space reconstruction using a geometrical construction[J].Physical Reviews A,1992,45(6):3403-3411.
[13]FRASER A,SWINUEY H.Independent coordinates for strange attractors from mutual information[J].Physical Reviews A,1986,33(2):1134-1140.
[14]OVERBEY L A,OLSON C C,TODD M D.A parametric investigation of state-space-based prediction error methods with stochastic excitation for structural health monitoring[J].Smart Materials&Structures,2007,16(5):1621-1638.
[15]NGUYEN S H,CHELIDZE D.New invariant measures to track slow parameter drifts in fast dynamical systems[J].Nonlinear Dynamics,2015,79(2):1207-1216.
[16]KEINER H,GADALA M S,TANG C W.Validity of boltremoval and gap-insertion techniques to experimentally simulate the vibration of a cracked rotor[J].Fatigue&Fracture of Engineering Materials&Structures,2004,27(6):449-458.
[17]QIU H,LEE J,LIN J,et al.Robust performance degradation assessment methods for enhanced rolling element bearing prognostics[J].Advanced Engineering Informatics,2003,17(3/4):127-140.
[18]QIU H,LEE J,LIN J,et al.Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J].Journal of Sound&Vibration,2006,289(4):1066-1090.
[19]QIAN Y,YAN R,HU S.Bearing degradation evaluation using recurrence quantification analysis and Kalman filter[J].IEEE Transactions on Instrumentation&Measurement,2014,63(11):2599-2610.
[20]JIANG H,CHEN J,DONG G,et al.Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis[J].Mechanical Systems&Signal Processing,2015(1):338-359.
[21]申中杰,陈雪峰,何正嘉,等.基于相对特征和多变量支持向量机的滚动轴承剩余寿命预测[J].机械工程学报,2013,49(2):183-189.SHEN Zhongjie,CHEN Xuefeng,HE Zhengjia,et al.Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine[J].Journal of Mechanical Engineering,2013,49(2):183-189.
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