Wind turbine gearbox fault diagnosis based on sound signal and improved MS-LMD
LIU Shaokang1, WU Yingjie1,2, AN Weilun1, XIN Hongwei1,2, YANG Yanjun1, WANG Jianguo1,2
1.School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China;
2.International Research Center of Precision Drive and Intelligent Control, Jilin 132012, China
摘要基于声音信号的机械故障诊断切实可行，但风电齿轮箱声音信号可拾取到机舱内的多个振源信号和干扰噪声，影响故障的精确定位，为此，提出一种改进的局部均值分解（local mean decomposition， LMD）方法，通过分离声音信号中的调频调幅成分，实现齿轮箱复合故障诊断。根据掩膜信号(masking signal ，MS)可抑制模态混叠的特点，采用二分法在幅值加权平均频率的1.0倍~1.5倍内搜索MS频率，提出MS与LMD多次结合的循环迭代方式，依次判断、抑制多组模态混叠现象。通过两类易发生模态混叠的仿真信号验证了该方法有效性，并将其应用于现场风电齿轮箱声音信号分析中，成功诊断出齿轮箱复合故障。
Abstract：Machinery’s fault diagnosis based on sound signal is feasible, but sound signal sensors of wind turbine gearbox can pick up multiple vibration source signals and interference noise in its engine room to affect accurate locating of faults.Here, an improved local mean decomposition (LMD) method was proposed to realize gearbox composite fault diagnosis through separating frequency modulation (FM) and amplitude modulation components of a sound signal.According to the feature of masking signal (MS) being able to suppress mode aliasing, the dichotomy method was used to search MS frequency in the range of 1.0-1.5 times of amplitude weighted average frequency.A cyclic iterative method to combine repeatedly MS and LMD was proposed to judge and suppress multi-group mode aliasing in turn.The effectiveness of the proposed method was verified using two kinds of simulated signals being easy to have modal aliasing.The proposed method was applied in sound signal analysis of an actual wind turbine gearbox to successfully diagnose its composite faults.
刘少康1，武英杰1,2，安伟伦1，辛红伟1,2,杨彦军1，王建国1,2. 基于声音信号和改进MS-LMD的风电齿轮箱故障诊断[J]. 振动与冲击, 2021, 40(11): 230-239.
LIU Shaokang1, WU Yingjie1,2, AN Weilun1, XIN Hongwei1,2, YANG Yanjun1, WANG Jianguo1,2. Wind turbine gearbox fault diagnosis based on sound signal and improved MS-LMD. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(11): 230-239.
［1］陈雪峰, 郭艳婕, 许才彬, 等.风电装备故障诊断与健康监测研究综述［J］.中国机械工程, 2020, 31(2): 175-189.
CHEN Xuefeng, GUO Yanjie, XU Caibin, et al.Review of fault diagnosis and health monitoring for wind power equipment［J］.China Mechanical Engineering, 2020, 31(2): 175-189.
［2］汤宝平, 罗雷, 邓蕾, 等.风电机组传动系统振动监测研究进展［J］.振动、测试与诊断, 2017, 37(3): 417-425.
TANG Baoping, LUO Lei, DENG Lei, et al.Research progress of vibration monitoring for wind turbine transmission system［J］.Journal of Vibration, Measurement and Diagnosis, 2017, 37(3): 417-425.
［3］FENG Z P, ZUO M J, JIAN Q, et al.Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes［J］.Mechanical Systems and Signal Processing, 2013, 40(1):56-75.
［4］FENG Z P, LIANG M, ZHANG Y, et al.Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation［J］.Renewable Energy, 2012, 47:112-126.
［5］WANG T Y, CHU F L, HAN Q K.Fault diagnosis for wind turbine planetary ring gear via a meshing resonance based filtering algorithm［J］.ISA Transactions, 2017, 67:173-182.
［6］LI Y Z, DING K, HE G L, et al.Vibration modulation sidebands mechanisms of equally-spaced planetary gear train with a floating sun gear［J］.Mechanical Systems and Signal Processing, 2019, 129:70-90.
［7］BAYDAR N，BALL A.A comparative study of acoustic and vibration signals in detection of gear failures using wigner-ville distribution［J］．Mechanical Systems and Signal Processing, 2001, 15(6):1091-1107．
［8］BAYDAR N，BALL A.Detection of gear failures via vibration and acoustic signals using wavelet transform［J］.Mechanical Systems and Signal Processing, 2001, 17(4):787-804.
［9］KIA S H，HENAO H, CAPOLINO G A.A comparative study of acoustic, vibration and stator current signatures for gear tooth fault diagnosis［C］∥2012 International Conference on Electrical Machines.Marseille: IEEE, 2012.
［10］武成同.基于声信号的风电机齿轮箱故障诊断研究［D］.大连:大连海事大学, 2018.
［11］田昊, 唐力伟, 陈红, 等.基于瞬态声与阶次倒谱的齿轮箱故障诊断［J］.振动、测试与诊断, 2009, 29(2)：137-140.
TIAN Hao,TANG Liwei, CHEN Hong, et al.Fault diagnosis of gearbox by using transient acoustic signal and order cepstrum［J］.Journal of Vibration, Measurement and Diagnosis, 2009, 29(2)：137-140.
［12］周艳玲, 杨德斌, 徐金梧, 等.基于声信号的轴承故障诊断方法［J］.振动与冲击, 2002,21(2): 21-23.
ZHOU Yanling, YANG Debin XU Jinwu,et al.A method for detection of defects in rolling element bearings based on acoustic signal analysis［J］.Journal of Vibration and Shock, 2002,21(2): 21-23.
［13］刘方, 沈长青, 何清波, 等.基于时域多普勒校正和EEMD的列车轴承道旁声音监测故障诊断方法研究［J］.振动与冲击, 2013, 32(24): 104-109.
LIU Fang, SHEN Changqing, HE Qingbo,et al.Wayside acoustic fault diagnosis for locomotive bearings based on Doppler effect correction and EEMD method in time domain［J］.Journal of Vibration and Shock, 2013, 32(24): 104-109.
［14］刘晶, 胡俊锋, 熊国良, 等.基于声音信号Teager能量算子解调的轮对轴承故障检测［J］.机床与液压, 2018, 46(7): 152-155.
LIU Jing, HU Junfeng, XIONG Guoliang, et al.Fault diagnosis detection of rolling bearing using demodulation based on the Teager energy operator［J］.Machine Tool ＆ Hydraulics, 2018, 46(7): 152-155.
［15］FENG Z P, ZHANG D, ZUO M J.Adaptive mode decomposition methods and their applications in signal analysis for machinery fault diagnosis: a review with examples［J］.IEEE Access, 2017,5: 24301-24331.
［16］SMITH J S.The local mean decomposition and its application to EEG perception data［J］.Journal of the Royal Society Interface, 2005,2(5): 443-454.
［17］肖瑛, 殷福亮.解相关EMD: 消除模态混叠的新方法［J］.振动与冲击, 2015, 34(4): 25-29.
XIAO Ying, YIN Fuliang.Decorrelation EMD:a new method of eliminating mode mixing［J］.Journal of Vibration and Shock, 2015, 34(4): 25-29.
［18］RYAN D, JAMES F K.The use of a masking signal toimprove empirical mode decomposition［C］∥IEEE International Conference on Acoustics, Speech，and Signal Processing.Philadelphia: IEEE, 2005．
［19］YANG Y L, DENG J B, KANG D L.An improved empirical mode decomposition by using dyadic masking signals［J］.Signal, Image and Video Processing, 2015, 9(6): 1259-1263.
［20］赵玲, 刘小峰, 秦树人, 等.消除经验模态分解中混叠现象的改进掩膜信号法［J］.振动与冲击, 2010, 29(9): 13-17.
ZHAO Ling, LIU Xiaofeng, QIN Shuren,et al.Use of masking signal to improve empirical mode decomposition［J］.Journal of Vibration and Shock, 2010, 29(9): 13-17.
［21］赵玲，秦树人，李宁，等.运用改进掩膜信号法的经验模态分解［J］.振动、测试与诊断, 2010, 30(4): 434-439.
ZHAO Ling, QING Shuren, LI Ning, et al.Use of an improved masking signal to improve empirical mode decomposition［J］.Journal of Vibration, Measurement and Diagnosis, 2010, 30(4): 434-439.
［22］王威, 陈熙源.基于改进掩膜EMD的光纤陀螺振动信号处理方法［J］.东南大学学报, 2018, 48(6): 1123-1129.
WANG Wei, CHEN Xiyuan.Vibration signal processing method for fog based on improved emd with masking signal［J］.Journal of Southeast University, 2018, 48(6): 1123-1129.
［23］SENROY N, SURYANARAYANAN S, RIBEIRO P F.An improved Hilbert-Huang method for analysis of time-varying waveforms in power quality［J］.IEEE Transactions on Power Systems, 2007, 22(4): 1843-1850.
［24］YANG Y L, MIAO C Y, DENG J H.Research on the mechanism of masking signals technique to improve empirical mode decomposition［J］.Journal of Information and Computational Science, 2012, 9: 3509-3516.
［25］王志坚, 吴文轩, 马维金, 等.基于LMD-MS的滚动轴承微弱故障提取方法［J］.振动、测试与诊断, 2018, 38(5): 1014-1020.
WANG Zhijian, WU Wenxuan, MA Weijin, et al.Fault signal extraction method of rolling bearing weak fault based on LMD-MS［J］.Journal of Vibration, Measurement and Diagnosis, 2018, 38(5): 1014-1020.
［26］SINGH D S, ZHAO Q.Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines［J］.Mechanical Systems and Signal Processing, 2016, 81:202-218.
［27］WU Z, HUANG N E.Ensemble empirical mode decomposition: a noise-assisteddata analysis method［J］.Advances in Adaptive Data Analysis, 2009,1:1-41.
［28］冯志鹏，赵镭镭，褚福磊.行星齿轮箱齿轮局部故障振动频谱特征［J］.中国电机工程学报，2013, 33(5): 119-127.
FENG Zhipeng, ZHAO Leilei, CHU Fulei.Vibration spectral characteristics of localized gear fault of planetary gearboxes［J］.Proceedings of the CSEE, 2013, 33(5): 119-127.
［29］冯志鹏，褚福磊.行星齿轮箱齿轮分布式故障振动频谱特征［J］.中国电机工程学报，2013, 33(2): 118-125.
FENG Zhipeng, CHU Fulei.Vibration spectral characteristics of distributed gear fault of planetary gearboxes［J］．Proceedings of the CSEE, 2013, 33(2): 118-125．
