压缩感知框架下的共振解调故障诊断方法
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摘要
风力机滚动轴承早期故障诊断中,压缩感知算法能够利用信号的稀疏性对信号去噪,但稀疏度的选取对去噪结果影响较大。由于信号故障成分在傅里叶域的稀疏度已知,故可通过傅里叶变换基和压缩感知子空间追踪(CS_SP)算法对风力机信号的包络特征进行不完全重构,以降低噪声和其他无关信息的影响,获取直接反映故障特征的信号成分,从而提取故障特征频率。研究结果表明,压缩感知框架下的的共振解调技术能有效获取风力机滚动轴承的故障特征信息,验证了所提方法的有效性。
In the early fault diagnosis of wind turbine rolling bearings,the compressive sensing algorithm might denoise the noisy signals utilizing the signal sparsity,but the selection of sparseness had a great influence on the denoising effectiveness.Since the sparseness of signal fault components was known in the Fourier domain,the envelope feature of wind turbine signals might be reconstructed by Fourier transform basis and compressive sensing subspace pursuit(CS_SP)algorithm.As a result,the effects of noises and other irrelevant informations would be reduced,and the fault characteristic frequency might be extracted from the signal components directly reflecting fault characteristics.The results demonstrate that the resonance demodulation technique under compressive sensing frameworks may effectively acquire the fault characteristic informations of wind turbine rolling bearings.The effectiveness of the proposed method was verified.
In the early fault diagnosis of wind turbine rolling bearings,the compressive sensing algorithm might denoise the noisy signals utilizing the signal sparsity,but the selection of sparseness had a great influence on the denoising effectiveness.Since the sparseness of signal fault components was known in the Fourier domain,the envelope feature of wind turbine signals might be reconstructed by Fourier transform basis and compressive sensing subspace pursuit(CS_SP)algorithm.As a result,the effects of noises and other irrelevant informations would be reduced,and the fault characteristic frequency might be extracted from the signal components directly reflecting fault characteristics.The results demonstrate that the resonance demodulation technique under compressive sensing frameworks may effectively acquire the fault characteristic informations of wind turbine rolling bearings.The effectiveness of the proposed method was verified.
引文
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[17]唐贵基,王晓龙.基于包络谱稀疏度和最大相关峭度解卷积的滚动轴承早期故障诊断方法[J].中国机械工程,2015,26(11):1450-1456.TANG Guiji,WANG Xiaolong.Diagnosis Method for Rolling Bearing Incipient Faults Based on Sparsity of Envelope Spectrum and Maximum Orrelated Kurtosis Deconvolution[J].China Mechanical Engineering,2015,26(11):1450-1456.
[18]刘金朝,丁夏完,王成国,等.自适应共振解调法及其在滚动轴承故障诊断中的应用[J].振动与冲击,2007,26(1):38-41.LIU Jinzhao,DING Xiawan,WANG Chengguo,et al.Adaptive Resonance Demodulation Method and Its Application to Fault Diagnosis of Freight Car Rolling Bearings[J].Journal of Vibration and Shock,2007,26(1):38-41.
[19]王平,廖明夫.滚动轴承故障诊断的自适应共振解调技术[J].航空动力学报,2005,20(4):606-612.WANG Ping,LIAO Mingfu.Adaptive Demodulated Resonance Technique for the Rolling Bearing Fault Diagnosis[J].Journal of Aerospace Power,2005,20(4):606-612.
[2]陈雪峰,李继猛,程航,等.风力发电机状态监测和故障诊断技术的研究与进展[J].机械工程学报,2011,47(9):45-52.CHEN Xuefeng,LI Jimeng,CHENG Hang,et al.Research and Application of Condition Monitoring and Fault Diagnosis Technology in Wind Turbines[J].Journal of Mechanical Engineering,2011,47(9):45-52.
[3]YI Cancan,LYU Yong,DANG Zhang,et al.Quaternion Singular Spectrum Analysis Using Convex Optimization and Its Application to Fault Diagnosis of Rolling Bearing[J].Measurement,2017,103:321-332.
[4]YI Cancan,LYU Yong,GE Mao,et al.Tensor Singular Spectrum Decomposition Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis[J].Entropy,2017,19(4):139.
[5]NIKOLAOU N G,ANTONIADIS I A.Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings Using Complex Shifted Morlet Wavelets[J].Mechanical Systems&Signal Processing,2002,16(4):677-694.
[6]高立新,王大鹏,刘保华,等.轴承故障诊断中共振解调技术的应用研究[J].北京工业大学学报,2007,33(1):1-5.GAO Lixin,WANG Dapeng,LIU Baohua,et al.Study on Application of Resonance-demodulation Technology in Rolling Bearing Fault Diagnosis[J].Journal of Beijing University of Technology,2007,33(1):1-5.
[7]CANDES E J,ROMBERG J,TAO T.Robust Uncertainty Principles:Exact Signal Reconstruction from Highly Incomplete Frequency Information[J].IEEE Transactions on Information Theory,2004,52(2):489-509.
[8]DONOHO D L.Compressed Sensing[J].IEEE Transactions on Information Theory,2006,2(4):1289-1306.
[9]郑毅贤.基于稀疏表示理论的图像去噪方法研究[D].上海交通大学,2013.ZHENG Yixian.Research on Image Denoising Method Based on Sparse Representation Theory[D].Shanghai:Shanghai Jiao Tong University,2013.
[10]孟宗,李晶,龙海峰,等.基于压缩信息特征提取的滚动轴承故障诊断方法[J].中国机械工程,2017,28(7):806-812.MENG Zong,LI Jing,LONG Haifeng,et al.Fault Diagnosis Method for Rolling Bearings Based on Compression Information Feature Extractions[J].China Mechanical Engineering,2017,28(7):806-812.
[11]杨海蓉,张成,丁大为,等.压缩传感理论与重构算法[J].电子学报,2011,39(1):142-148.YANG Hairong,ZHANG Cheng,DING Dawei,et al.The Theory of Compressed Sensing and Reconstruction Algorithm[J].Acta Electronica Sinica,2011,39(1):142-148.
[12]刘冰,付平,孟升卫.基于正交匹配追踪的压缩感知信号检测算法[J].仪器仪表学报,2010,31(9):1959-1964.LIU Bing,FU Ping,MENG Shengwei.Compressive Sensing Signal Detection Algorithm Based on Orthogonal Matching Pursuit[J].Chinese Journal of Scientific Instrument,2010,31(9):1959-1964.
[13]NEEDELL D,TROPP J A.Co Sa MP:Iterative Signal Recovery from Incomplete and Inaccurate Samples[J].Applied&Computational Harmonic Analysis,2009,26(3):301-321.
[14]WEI D,MILENKOVIC O.Subspace Pursuit for Compressive Sensing Signal Reconstruction[J].IEEE Transactions on Information Theory,2009,55(5):2230-2249.
[15]唐刚,杨琴,侯伟,等.基于稀疏采样的轴承故障特征识别方法研究[J].振动与冲击,2014,33(增):196-199.TANG Gang,YANG Qin,HOU Wei,et al.A Bearing Fault Detection Method Based on Sparse Signal Samples[J].Journal of Vibration and Shock,2014,33(S):196-199.
[16]刘国海.基于压缩感知理论的电力系统数据检测与压缩方法研究[D].镇江:江苏大学,2012.LIU Guohai.Research on Data Detection and Compression Method of Power System Based on Compressed Sensing Theory[D].Zhenjiang:Jiangsu University,2012.
[17]唐贵基,王晓龙.基于包络谱稀疏度和最大相关峭度解卷积的滚动轴承早期故障诊断方法[J].中国机械工程,2015,26(11):1450-1456.TANG Guiji,WANG Xiaolong.Diagnosis Method for Rolling Bearing Incipient Faults Based on Sparsity of Envelope Spectrum and Maximum Orrelated Kurtosis Deconvolution[J].China Mechanical Engineering,2015,26(11):1450-1456.
[18]刘金朝,丁夏完,王成国,等.自适应共振解调法及其在滚动轴承故障诊断中的应用[J].振动与冲击,2007,26(1):38-41.LIU Jinzhao,DING Xiawan,WANG Chengguo,et al.Adaptive Resonance Demodulation Method and Its Application to Fault Diagnosis of Freight Car Rolling Bearings[J].Journal of Vibration and Shock,2007,26(1):38-41.
[19]王平,廖明夫.滚动轴承故障诊断的自适应共振解调技术[J].航空动力学报,2005,20(4):606-612.WANG Ping,LIAO Mingfu.Adaptive Demodulated Resonance Technique for the Rolling Bearing Fault Diagnosis[J].Journal of Aerospace Power,2005,20(4):606-612.