一种改进的峭度图方法及其在复杂干扰下轴承故障诊断中的应用

顾晓辉1,2, 杨绍普1,2, 刘永强2, 廖英英2

振动与冲击 ›› 2017, Vol. 36 ›› Issue (23) : 187-193.

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (23) : 187-193.
论文

一种改进的峭度图方法及其在复杂干扰下轴承故障诊断中的应用

  • 顾晓辉1,2, 杨绍普1,2, 刘永强2, 廖英英2
作者信息 +

An improved kurtogram method and its application in fault diagnosis of rolling element bearings under complex interferences

  • GU Xiao-hui 1,2 , YANG Shao-pu 1,2 , LIU Yong-qiang 2 , LIAO Ying-ying 2
Author information +
文章历史 +

摘要

快速峭度图是一种常用的滚动轴承故障诊断方法,但由于峭度指标对冲击过于敏感,在干扰较复杂的工况中,该方法往往无法正确识别出最优的共振频带进行包络解调。然而,解调信号的包络谱对噪声具有一定的免疫能力,而且包络谱中通常会清晰的出现故障特征频率及其倍频成分,呈现出典型的周期性脉冲特点。因此,提出应用相关峭度定量地刻画窄带信号的包络谱幅值,即以频域相关峭度值生成峭度图,用于最优频带的自适应地识别。同时,基于相关峭度的指向性,可以将该方法应用于轴承的复合故障诊断。最后通过实验分析,验证了本文方法对轴承微弱故障和复合故障诊断的有效性。

Abstract

Fast Kurtogram is one of the most useful methods in fault diagnosis of rolling element bearings. However, in some cases of complex interferences, it cannot exactly recognize the optimal resonance frequency band for envelope demodulation due to that the kurtosis index is too sensitive to impulsive noise. In fact, the envelope spectrum of demodulated signals in frequency domain has a certain immunity ability to noise, the bearing fault characteristic frequency and its harmonics often appear clearly with typical periodic impulse features in the envelope spectrum. Here, the frequency domain correlated kurtosis was proposed to quantitatively describe envelope spectrum amplitudes of narrow-band signals and generate a kurtogram. Simultaneously, the proposed method was applied in the compound fault detection based on the directivity of correlated kurtosis. In addition, two cases of real bearing fault signals were employed to verify the effectiveness and robustness of the proposed method in bearing weak fault diagnosis and compound fault diagnosis.

关键词

峭度图 / 频域相关峭度 / 包络分析 / 滚动轴承 / 故障诊断

Key words

kurtogram / frequency domain correlated kurtosis / envelope analysis / rolling element bearing / fault diagnosis

引用本文

导出引用
顾晓辉1,2, 杨绍普1,2, 刘永强2, 廖英英2. 一种改进的峭度图方法及其在复杂干扰下轴承故障诊断中的应用[J]. 振动与冲击, 2017, 36(23): 187-193
GU Xiao-hui 1,2,YANG Shao-pu 1,2,LIU Yong-qiang 2,LIAO Ying-ying 2. An improved kurtogram method and its application in fault diagnosis of rolling element bearings under complex interferences[J]. Journal of Vibration and Shock, 2017, 36(23): 187-193

参考文献

[1] McFadden P D, Smith J D. Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review[J]. Tribology international, 1984, 17(1): 3-10.
[2] Antoni J. The spectral kurtosis: a useful tool for characterising non-stationary signals[J]. Mechanical Systems and Signal Processing, 2006, 20(2): 282-307.
[3] 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.
[4] Antoni J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1): 108-124.
[5] 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: 679-698.
[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] 王晓冬, 何正嘉, 訾艳阳. 滚动轴承故障诊断的多小波谱峭度方法[J]. 西安交通大学学报, 2010, 44(3): 77-81.
WANG Xiao-dong, HE Zheng-jia, ZI Yan-yang. Spectral kurtosis of multiwavelet for fault diagnosis of rolling bearing[J]. Journal of Xi'an Jiaotong University, 2010, 44(3): 77-81.
[8] 田福庆, 罗荣, 李万, 等. 改进的谐波小波包峭度图及其应用[J]. 上海交通大学学报, 2014, 48(1): 39-44.
TIAN Fu-qing, LUO Rong, LI Wan, et al. Improved harmonic wavelet packet kurtogram and its application[J]. Journal of Shanghai Jiaotong University, 2014, 44(1): 39-44.
[9] Wang Y, Liang M. An adaptive SK technique and its application for fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2011, 25(5): 1750-1764.
[10] Li C, Cabrera D, de Oliveira J V, et al. Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram[J]. Mechanical Systems and Signal Processing, 2016, 76: 157-173.
[11] 马新娜, 杨绍普. 典型谱峭图在共振解调方法中的应用[J]. 振动. 测试与诊断, 2015, 35(6): 1140-1144.
MA Xin-na, YANG Shao-pu. Study and application of demodulated resonance based on typi-kurtogram[J]. Journal of Vibration, Measurement & Diagnosis, 2015, 35(6): 1140-1144.
[12] Yu G, Li C, Zhang J. A new statistical modeling and detection method for rolling element bearing faults based on alpha–stable distribution[J]. Mechanical Systems and Signal Processing, 2013, 41(1): 155-175.
[13] Obuchowski J, Wyłomańska A, Zimroz R. Selection of informative frequency band in local damage detection in rotating machinery[J]. Mechanical Systems and Signal Processing, 2014, 48(1): 138-152.
[14] 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, 34(7): 98-102.
DAI Shi-chao, GUO Yu, WU Xing, et al. Improvement on fast kurtogram algorithm based on sub-frequency-ban spectral kurtosis average[J]. Journal of Vibration and Shock, 2015, 34(7): 98-102.
[15] Zhang X, Kang J, Xiao L, et al. A new improved kurtogram and its application to bearing fault diagnosis[J]. Shock and Vibration, 2015, 2015: 1-22.
[16] 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.
[17] Barszcz T, JabŁoński 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.
[18] Wang D, Peter W T, Tsui K L. An enhanced Kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2013, 35(1): 176-199.
[19] Peter W T, 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.
[20] Chen B Q, Zhang Z S, Zi Y Y, et al. Detecting of transient vibration signatures using an improved fast spatial–spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery[J]. Mechanical Systems and Signal Processing, 2013, 40(1): 1-37.
[21] Antoni J. The infogram: Entropic evidence of the signature of repetitive transients[J]. Mechanical Systems and Signal Processing, 2016, 74: 73-94.
[22] Ho D, Randall R B. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals[J]. Mechanical systems and signal processing, 2000, 14(5): 763-788.

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