基于QGA优化广义S变换的滚动轴承故障特征提取

王波1 刘树林2 张宏利2

振动与冲击 ›› 2017, Vol. 36 ›› Issue (5) : 108-113.

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (5) : 108-113.
论文

基于QGA优化广义S变换的滚动轴承故障特征提取

  • 王波1  刘树林2 张宏利2
作者信息 +

Fault feature extraction for rolling bearings  based on generalized S transformation optimized with Quantum genetic algorithm

  • WANG Bo1,LIU Shulin2,ZHANG Hongli2
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文章历史 +

摘要

考虑到实际工程环境中噪声对故障特征提取的影响,提出了基于量子遗传算法(QGA)优化广义S变换的滚动轴承故障特征提取方法。该方法以时频分布集中程度为评价标准,首先采用量子遗传算法自适应地选取广义S变换中最优窗口控制参数,然后提取信号变换后复时频矩阵的模向量作为滚动轴承故障特征向量。利用该方法提取的滚动轴承故障特征与其它故障特征进行故障识别对比研究,实验结果表明该方法能够更准确地提取出故障特征,验证了方法的优越性。此外,对不同噪声强度背景下的滚动轴承振动信号进行故障特征提取,诊断结果进一步显示所提方法具有良好的抗噪性和健壮性。

Abstract

Considering noise’s effects  on fault feature extraction of rolling bearings in practical engineering environment,a novel method for rolling bearing fault feature extraction based on the generalized S transformation optimized with the quantum genetic algorithm (QGA) was proposed.Firstly,the optimal window control parameters of the generalized S transformation were selected adaptively with QGA taking the concentration the level of time-frequency distribution as the evaluation standard.Then the mode vectors of the complex time-frequency matrix formed after fault vibration signals of rolling bearings were transformed with the generalized S transformation were  extracted as rolling bearing fault feature vectors.The method was applied to extract rolling bearing fault feature and compared with other methods using fault diagnosis tests.The results showed that the proposed method can extract fault features more accurately than other methods can.Moreover,the fault feature extraction tests of rolling bearing vibration signals under different levels of background noise indicated that the proposed method has a good anti-noise capability and a strong robustness.

关键词

广义S变换;量子遗传算法 / 滚动轴承;故障诊断;特征提取

Key words

generalized S transform / quantum genetic algorithm / rolling bearing / fault diagnosis / feature extraction

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王波1 刘树林2 张宏利2 . 基于QGA优化广义S变换的滚动轴承故障特征提取[J]. 振动与冲击, 2017, 36(5): 108-113
WANG Bo1,LIU Shulin2,ZHANG Hongli2. Fault feature extraction for rolling bearings  based on generalized S transformation optimized with Quantum genetic algorithm[J]. Journal of Vibration and Shock, 2017, 36(5): 108-113

参考文献

 [1] Ekici S, Yildirim S, Poyraz M. Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition[J]. Expert systems with applications, 2008,34(4):2937-2944.
 [2] 李辉, 郑海起, 唐力伟. 基于双树复小波包峭度图的轴承故障诊断研究[J]. 振动与冲击, 2012(10):13-18.
LI Hui, ZHENG Hai-qi, TANG Li-wei. Bearing fault diagnosis based on kurtogram of dual-tree complex wavelet packet tansform[J]. Journal of vibration and shock, 2012(10):13-18.
 [3] He Q B. Vibration signal classification by wavelet packet energy flow manifold learning[J]. Journal of sound and vibration, 2013,332(7):1881-1894.
 [4] 沈长青, 谢伟达, 朱忠奎, 等. 基于EEMD和改进的形态滤波方法的轴承故障诊断研究[J]. 振动与冲击, 2013,32(2):39-43.
SHEN Chang-qing, Peter W.Tse, ZHU Zhong-kui, et al. Rolling element bearing fault diagnosis based on EEMD and improved morphological filtering method[J]. Journal of vibration and shock, 2013(2):39-43.
 [5] 王录雁, 王强, 张梅军, 等. 基于 EMD 的滚动轴承故障灰色诊断方法[J]. 振动与冲击, 2014(03):197-202.
WANG Lu-yan, WANG Qiang, ZHANG Mei-jun,et al. A grey fault diagnosis method for rolling bearings based on EMD[J].Journal of vibration and shock, 2014(03):197-202.
 [6] Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: The S transform[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1996,44(4):998-1001.
 [7] 占勇, 程浩忠, 丁屹峰, 等. 基于S变换的电能质量扰动支持向量机分类识别[J]. 中国电机工程学报, 2005(04):53-58.
ZHAN Yong, CHENG Hao-zhong, DING Yi-feng, et al. S-transform-based classification of power quality disturbance
signals by support vector machines[J].Proceedings of the CSEE, 2005(04):53-58.
 [8] Drabycz S, Stockwell R G, Mitchell J R. Image Texture Characterization Using the Discrete Orthonormal S-Transform[J]. Journal of digital imaging, 2009,22(6):696-708.
 [9] Mahmood M T, Choi T S. Focus measure based on the energy of high-frequency components in the S transform[J]. Optics letters, 2010,35(8):1272-1274.
[10] 蒋模华, 陈文静, 郑志平. 基于S变换的解相技术研究[J]. 光学学报, 2011(04):101-109.
JIANG Mo-hua CHEN Wen-jing ZHENG Zhi-ping. Research of phase demodulation technique based on S-transform [J]. Optica Sinica, 2011(04):101-109.
[11] Zhong M, Chen W J, Wang T, et al. Application of two-dimensional S-Transform in fringe pattern analysis[J]. Optics and lasers in engineering, 2013,51(10):1138-1142.
[12] Sanchez P, Montoya F G, Manzano-Agugliaro F, et al. Genetic algorithm for S-transform optimisation in the analysis and classification of electrical signal perturbations[J]. Expert systems with applications, 2013,40(17):6766-6777.
[13] Dash P K, Panigrahi B K, Panda G. Power quality analysis using S-Transform[J]. IEEE Transactions on power delivery, 2003,18(2):406-411.
[14] Djurovic I, Sejdic E, Jiang J. Frequency-based window width optimization for S-transform[J]. Aeu-international journal of electronics and communications, 2008,62(4):245-250.
[15] Stankovic L. A measure of some time-frequency distributions concentration[J]. Signal processing, 2001,81(3):621-631.
[16] 王凌. 量子进化算法研究进展[J]. 控制与决策, 2008,23(12):1321-1326.
WANG Ling. Advances in quantum-inspired evolutionary algorithms[J]. Control and Decision, 2008,23(12):1321-1326.
[17] 杨俊安, 庄镇泉, 史亮. 多宇宙并行量子遗传算法[J]. 电子学报, 2004(06):923-928.
     YANG Jun-an, ZHUANG Zhen-quan, SHI Liang. Multi-Universe Parallel Quantum Genetic Algorithm[J]. Acta Electronica Sinica, 2004(06):923-928.

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