利用峭度指标识别滚动轴承共振频带，结合包络分析解调故障特征，是滚动轴承故障诊断的常用方法。峭度指标虽然能够表征瞬态冲击特征的强弱，却无法利用瞬态冲击特征循环发生的特点，导致其难以区分脉冲噪声和循环瞬态冲击，无法准确识别共振频带，进而容易导致错误的故障诊断结果。受峭度和信号自相关的启发，重新定义相关峭度，提出平方包络谱相关峭度新指标；并结合Morlet小波滤波和粒子群优化算法，提出一种滚动轴承最优共振解调方法。通过与峭度、谱峭度等进行对比，仿真和试验分析结果表明平方包络谱相关峭度能够准确识别循环瞬态冲击；最优共振解调能够稳健确定共振频带的最优中心频率和带宽，准确解调诊断滚动轴承故障，验证了平方包络谱相关峭度在检测循环瞬态冲击和识别最优共振频带中的有效性和优越性。
It is a common practice of rolling bearing fault diagnosis by choosing rolling bearing resonance band based on kurtosis index and demodulating fault features. Kurtosis can only represent the strength of transients' features, but it cannot differentiate impulse noise and transients, which is cyclical generated in rolling bearing vibration signals. As a result, it leads to the inaccurate identification results of the rolling bearing's resonance band and the unsatisfactory demodulation results of the fault features. In order to overcome these shortcomings, this manuscript redefined the correlated kurtosis and put forward the constructor method of correlated kurtosis in squared envelope spectrum. Combining Morlet wavelet filtering and particle swarm optimization, the proposed method can adaptively choose the optimal resonance band of rolling bearings and demodulate rolling bearing's fault features. The analysis results of simulation data and test data showed that compared with index, such as kurtosis and envelope spectral kurtosis, the proposed correlated kurtosis in squared envelope spectrum can overcome the lack of kurtosis-based index. Moreover, combining optimal resonance frequency demodulation, it can select the exact optimal resonance band's central frequency and bandwidth of the rolling bearing. Improved results of the rolling bearing fault diagnosis verified the validity and advantage of this method.
[1] SMITH W A,RANDALL R B.Rolling element bearing diagnostics using the Case Western Reserve University data:A benchmark study[J].Mechanical Systems and Signal Processing,2015,64-65:100-131.
[2] CHEN X,FENG F,ZHANG B.Weak fault feature extraction of rolling bearings based on an improved kurtogram[J].Sensors,2016,16(9):1482.
[3] ANTONI J.The spectral kurtosis:A useful tool for characterising non-stationary signals[J].Mechanical Systems and Signal Processing,2006,20(2):282-307.
[4] ANTONI J,RANDALL R B.The spectral kurtosis:Application to the vibratory surveillance and diagnostics of rotating machines[J].Mechanical Systems and Signal Processing,2006,20(2):308-331.
[5] ANTONI J.Fast computation of the kurtogram for the detection of transient faults[J].Mechanical Systems and Signal Processing,2007,21(1):108-124.
[6] LEI Y,LIN J,HE Z,et al.Application of an improved kurtogram method for fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2011,25(5):1738-1749.
[7] WANG D,TSE P W,TSUI K L.An enhanced kurtogram method for fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2013,35(1-2):176-199.
[8] 代士超,郭瑜,伍星,等.基于子频带谱峭度平均的快速谱峭度图算法改进[J].振动与冲击,2015(7):98-102. DAI Shichao,GUO Yu,WU Xing,et al.Improvement on fast kurtogram algorithm based on sub-frequency-band spectral kurtosis average[J].Journal of Vibration and Shock,2015(7):98-102.
[9] 马新娜,杨绍普.典型快速谱峭图算法的研究及应用[J].振动与冲击,2016(15):109-114. MA Xinna,YANG Shaopu.Typical fast kurtogram algorithm and its application[J].Journal of Vibration and Shock,2016(15):109-114.
[10] 李川,朱荣荣,杨帅.基于多指标模糊融合的滚动轴承诊断的最优频带解调方法[J].机械工程学报,2015,51(7):107-114. LI Chuan,ZHU Rongrong,YANG Shuai.Optimal frequency band demodulation for fault diagnosis of rolling element bearings based on fuzzy fusion of multiple criteria[J].Journal of Mechanical Engineering,2015,51(7):107-114.
[11] BARSZCZ T,JABŁONSKI A.A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram[J].Mechanical Systems and Signal Processing,2011,25(1):431-451.
[12] 张龙,熊国良,黄文艺.复小波共振解调频带优化方法和新指标[J].机械工程学报,2015,51(3):129-138. ZHANG Long,XIONG Guoliang,HUANG Wenyi.A new procedure and index for the parameter optimization of complex wavelet based resonance demodulation[J]. Journal of Mechanical Engineering,2015,51(3):129-138.
[13] TSE P W,WANG D.The design of a new sparsogram for fast bearing fault diagnosis:Part 1 of the two related manuscripts that have a joint title as "Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement-Parts 1 and 2"[J].Mechanical Systems and Signal Processing,2013,40(2):499-519.
[14] TSE P W,WANG D.The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection[J].Mechanical Systems and Signal Processing,2013,40(2):520-544.
[15] ANTONI J. The infogram:Entropic evidence of the signature of repetitive transients[J].Mechanical Systems and Signal Processing,2016,74:73-94.
[16] LI C,LIANG M,WANG T.Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals[J].Mechanical Systems and Signal Processing,2015,64-65:132-148.
[17] WANG Y, XIANG J, MARKERT R,et al.Spectral kurtosis for fault detection,diagnosis and prognostics of rotating machines:A review with applications[J]. Mechanical Systems and Signal Processing,2016,66-67:679-698.
[18] MCDONALD G L,ZHAO Q,ZUO M J.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems and Signal Processing,2012,33:237-255.
[19] 唐贵基,王晓龙.最大相关峭度解卷积结合1.5维谱的滚动轴承早期故障特征提取方法[J].振动与冲击,2015(12):79-84. TANG Guiji, WANG Xiaolong. Feature extraction for rolling bearing incipient fault based on maximum correlated kurtosis deconvolution and 1.5 dimension spectrum[J].Journal of Vibration and Shock,2015(12):79-84.
[20] 唐贵基,王晓龙.最大相关峭度解卷积结合稀疏编码收缩的齿轮微弱故障特征提取[J].振动工程学报,2015,28(3):478-486. TANG Guiji,WANG Xiaolong.Weak feature extraction of gear fault based on maximum correlated kurtosis deconvolution and sparse code shrinkage[J].Journal of Vibration Engineering,2015,28(3):478-486.
[21] 唐贵基,王晓龙.自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J].中国电机工程学报,2015(6):1436-1444. TANG Guiji,WANG Xiaolong.Adaptive maximum correlated kurtosis deconvolution method and its application on incipient fault diagnosis of bearing[J].Proceedings of the CSEE,2015(6):1436-1444.
[22] 丁康,黄志东,林慧斌.一种谱峭度和Morlet小波的滚动轴承微弱故障诊断方法[J].振动工程学报,2014(1):128-135. DING Kang,HUANG Zhidong,LIN Huibin.A weak diagnosis method for rolling element bearings based on Morlet wavelet and spectral kurtosis[J].Journal of Vibration Engineering,2014(1):128-135.
[23] 张菀,贾民平,朱林.一种自适应Morlet小波滤波方法及其在滚动轴承早期故障特征提取中的应用[J].东南大学学报,2016(3):457-463. ZHANG Yu,JIA Minping,ZHU Lin.An adaptive Morlet wavelet filter method and its application in detecting early fault feature of ball bearings[J].Journal of Southeast University,2016(3):457-463.
[24] BORGHESANI P,PENNACCHI P,CHATTERTON S.The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings[J].Mechanical Systems and Signal Processing,2014,43(1-2):25-43.
