滚动轴承振动信号往往信噪比较低，且具有较强的非高斯噪声，如何选择合适的解调频带一直是故障诊断的难点。自相关谱峭度图（Autogram）是新提出的一种最优频带选择方法，通过计算解调信号的平方包络的无偏自相关的峭度，能够有效地检测到解调频带及其故障频率。但此方法易受到噪声干扰，故障特征识别不明显。基于此，提出了一种基于最小熵解卷积（MED）与Autogram的滚动轴承故障诊断方法。该方法通过MED去除噪声，在得到最佳频带的同时，能够有效地突显故障特征。通过分析仿真信号及实验数据，将所提方法与快速谱峭度及现有方法进行了对比，结果表明，所提故障诊断方法能够准确地检测到解调频带及故障频率，突出故障特征和提高故障检测效果。
Abstract
The vibration signal of rolling bearing generally has low signal-to-noise ratio and strong non-Gaussian noise. How to accurately select the demodulation frequency band is still a difficult problem of fault diagnosis of rolling bearing. Autogram is a new optimal band selection method. By calculating the kurtosis of unbiased autocorrelation of the squared envelope of the demodulated signal, the demodulation band and its fault frequency can be effectively detected. But, Autogram is susceptible to noise and the fault feature is not obvious. To overcome this, a new fault diagnosis method for rolling bearing based on the minimum entropy deconvolution (MED) and Autogram is proposed. The proposed method can effectively highlight the fault characteristics and obtain the best band for demodulation. The proposed method is compared with the fast spectral kurtosis and the existing methods by simulation and experimental data of rolling bearing analysis. The results show that the proposed fault diagnosis method for rolling bearing can accurately detect the demodulation frequency band, highlight the fault frequency and improve the effect of fault detection.
关键词
最小熵解卷积 /
自相关谱峭度图 /
快速谱峭度 /
解调频带 /
滚动轴承
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Key words
Minimum entropy deconvolution /
Autogram /
Fast spectral kurtogram /
demodulation band
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参考文献
[1] 梅宏斌. 滚动轴承振动检测与诊断 [M]. 北京：机械工业出版社，1995.
MEI Hongbin. Rolling bearing vibration detection and diagnosis [M]. Beijing: Mechanical Industry Press,1995.
[2] 荆双喜，杨鑫，冷军发，等. 基于改进EMD与谱峭度的滚动轴承故障特征提取[J].机械传动，2016,40（4）：125-128.
JING Shuangxi, YANG Xin, LENG Junfa, et al. Fault feature extraction of rolling bearing based on improved EMD and spectrum kurtosis [J]. Journal of Mechanical Transmission,2016,40(4):125-128.
[3] 刘永强，杨绍普，廖英英，等. 一种自适应共振解调方法及其在滚动轴承早期故障诊断中的应用[J].振动工程学报，2016, 9（2）：366-370.
LIU Yongqiang,YANG Shaopu,LIAO Yingying, et al. An Adaptive Resonance Demodulation Method and Its Application in Early Fault Diagnosis of Rolling Bearings[J]. Journal of Vibration Engineering,2016,9(2):366-370.
[4] 郑近德，程军圣，杨宇. 一种新的估计瞬时频率的方法-经验包络法[J].振动与冲击，2012,31（17）：86-90.
ZHENG Jinde,CHEN Junsheng,YANG Yu. A New Method for Estimating Instantaneous Frequency-Experience Envelope Method[J]. Journal of Vibration and Shock,2012,31(17): 86-90.
[5] R.B. Randall, J. Antoni. Rolling element bearing diagnostics–a tutorial[J]. Mechanical Systems and Signal Process, 2011,25 (2):485–520.
[6] 王莉. 基于阶比峭度图的变转速下滚动轴承故障特征提取方法研究[D].重庆：重庆大学， 2015.
WANG Li. Research on Fault Feature Extraction Method of Rolling Bearings under Variable Speed Based on Order Scalar Diagram[D]. Chongqing：Chongqing university, 2015.
[7] R.B.Randall，J.Antoni. Rolling element bearing diagnostics-A tutorial[J].Mechanical Systems and Signal Process，2011, 25:485-520.
[8] J.Antoni. The sprctral kurtosis: a useful tool for characterizing non-stationary signals[J]. Mechanical Systems and Signal Process, 2006,20(2):282-307.
[9] J. Antoni. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Process, 2007,21(1): 108–124.
[10] Y. Lei, J. Lin, Z. He, Y. Zi. Application of an improved kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Process, 2011,25 (5):1738–1749.
[11] 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.
[12] D. Wang, W.T. Peter, K.L. Tsui. An enhanced kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Process, 2013,35(1):176–199.
[13] Ail Moshrefzadeh, Alessandro Fasana. The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis[J]. Mechanical Systems and Signal Processing, 2018, 105:294-318.
[14] 王宏超，陈进，董广明. 基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报，2013,49（1）：88-94.
WANG Hongchao, CHEN Jin, DONG Guangming. Feature Extraction of Weak Faults of Rolling Bearings Based on Minimum Entropy Deconvolution and Sparse Decomposition[J]. Journal of Mechanical Engineering,2013,49(1):88-94.
[15] A. Moshrefzadeh, A. Fasana, L. Garibaldi. Using unbiased autocorrelation to enhance kurtogram and envelope analysis results for rolling element bearing diagnostics[J]. in: International Conference Surveillance 9, 2017,Morocco.
[16] 周航. 基于改进最大重叠离散小波包变换的线性系统参数识别[D].南京：南京航空航天大学，2018.
ZHOU Hang. Parameter Identification for Linear SystemsdaptiBased on Improved Maximal Overlap Discrete Wavelet Packet Transform[D]. Nanjing: Nanjing University of Aeronautics and Astronautics,2018.
[17] A.T. Walden. Wavelet analysis of discrete time series[J]. in: European Congress of Mathematics 2001, pp. 627–641. Springer.
[18] Hai Qiu, Jay Lee, Jing Lin. Wavelet filter-based weak signature detection method and its application on rolling bearing element bearing prognostics[J]. Journal of Sound and Vibration，2005，289:1066-1090.
[19] R.B. Randall, J. Antoni, S. Chobsaard. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals[J]. Mechanical Systems and Signal Process,2011,15(5):945–962.
[20] Haiyang Liu, Weiguo Huang, Shibin Wang, Zhongkui Zhu. Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection[J]. Signal Processing, 2014, 96: 118-124.
[21] 江瑞龙. 基于最小熵解卷积的滚动轴承故障诊断研究[D].上海：上海交通大学，2013.
JIANG Ruilong. Research on Fault Diagnosis of Rolling Bearing Based on Minimum Entropy Deconvolution[D]. Shanghai: Shanghai Jiao Tong University,2013.
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