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| Rolling bearing fault feature extraction method based on modulation enhanced slice MSB |
| FENG Kun1, YAN Kang1, HU Minghui2, HE Ya1,2, JIANG Zhinong1,2 |
1. MOE Key Lab of Engine Health Monitoring-Control and Networking, Beijing 100029, China;
2. Beijing Municipal Key Lab of Health Monitoring and Self-healing of High-level Mechanical Equipment, Beijing 100029, China |
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Abstract Aiming at poor performance of the fast spectral kurtosis diagram and the traditional slice MSB (modulation signal bispectrum) algorithm in extracting bearing fault features under strong interference conditions, a rolling bearing fault feature extraction method based on the modulation enhanced slice MSB algorithm was proposed. Firstly, MSB algorithm was used to do calculation and obtain MSB of the original vibration signal, and the carrier spectrum was obtained by slicing and superposing main dimension slices. Secondly, based on the property of MSB highlighting modulation characteristics of rolling bearing fault signal, the slicing range was optimized using the PSO algorithm. Finally, the bispectral coherence function of the slice where fault feature was located and the modulated signal bispectrum were combined to get the modulation spectrum through the enhanced reconstruction. Thus, most of noise components were removed, and fault features were extracted directly. The results of simulation and tests showed that the proposed modulation enhanced slice MSB algorithm can realize rolling bearing fault feature extraction under conditions of long transmission path and strong noise interference; the results obtained with this algorithm are more intuitive and clearer than those obtained with the fast spectral kurtosis diagram.
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Received: 12 March 2020
Published: 15 July 2021
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［1］JIANG Zhinong, HU Minghui, FENG Kun. Weak fault feature extraction scheme for intershaft bearings based on linear prediction and order tracking in the rotation speed difference domain［J］. Applied Science, 2017, 9(937):1-26.
［2］RANDALL R B, ANTONI J. Rolling bearing diagnostics: a tutorial［J］. Mechanical Systems and Signal Processing, 2010, 25(11):485-520.
［3］DWYER R F. Detection of non-Gaussian signals by frequency domain kurtosis estimation［C］// Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. Boston, MA, 1983.
［4］ANTONI J. The spectral kurtosis: a useful tool for characterising nonstationary signals［J］. Mechanical Systems and Signal Processing, 2006, 20:282-307.
［5］ANTONI J, RANDALL R B. The spectral kurtosis: application to the vibrationary surveillance and diagnostics of rotating machines［J］. Mechanical Systems and Signal Process, 2006, 20:308-331.
［6］ANTONI J. Fast computation of the kurtogram for the detection of transient faults［J］. Mechanical Systems and Signal Processing, 2006, 21:108-124.
［7］KENNEDY J, EBERHART R C. Particle swarm optimization［J］. IEEE International Conference on Neural Networks, 1995(4):1942-1948.
［8］王建东, 马增强, 王梦奇, 等. 粒子群结合形态学滤波的滚动轴承故障诊断［J］. 轴承, 2017(12):49-52.
WANG Jiandong, MA Zengqiang, WANG Mengqi, et al. Fault diagnosis of rolling bearing based on particle swarm optimization and morphological filter［J］. Bearing, 2017(12):49-52.
［9］毛海波, 周凤星. 基于粒子群的共振解调滚动轴承故障诊断研究［J］. 组合机床与自动化加工技术, 2019(9):46-49.
MAO Haibo, ZHOU Fengxing. Research on fault diagnosis of rolling bearings for the resonant demodulation method based on particle swarm optimization［J］. Modular Machine Tool & Automatic Manufacturing Technique, 2019(9):46-49.
［10］GUO W, TSE P W, DJORDJEVICH A. Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition［J］. Measurement, 2012, 45(5):1308-1322.
［11］唐贵基，王晓龙，邓飞跃. 改进增强峭度图和增强包络谱在滚动轴承故障诊断上的应用［J］. 振动与冲击，2014, 33(13): 53-58.
TANG Guiji, WANG Xiaolong, DENG Feiyue. Application of improved enhanced kurtogram and enhanced envelope spectrum in fault diagnosis of rolling bearings［J］. Journal of Vibration and Shock, 2014, 33(13): 53-58.
［12］QIANG L, YI D Y. A co-evolving framework for robust particle swarm optimization［J］. Applied Mathematics and Computation, 2006, 179(1):135-146.
［13］唐贵基, 王晓龙. 基于局部均值分解和切片双谱的滚动轴承故障诊断研究［J］. 振动与冲击, 2013, 32(24):83-95.
TANG Guiji, WANG Xiaolong. Fault diagnosis of roller bearings based on local mean decomposition and slice bispectrum［J］. Journal of Vibration and Shock, 2013, 32(24):83-95.
［14］周宇, 陈进, 董广明, 等. 基于循环双谱二次切片分析的滚动轴承故障诊断研究［J］. 振动与冲击, 2012, 31(18):24-28.
ZHOU Yu, CHEN Jin, DONG Guangming, et al. Fault diagnosis of a rolling element bearing based on twice-slice of a cyclic bispectrum［J］. Journal of Vibration and Shock, 2012, 31(18): 24-28.
［15］蒋永华, 李荣强, 焦卫东, 等. 应用EMD和双谱分析的故障特征提取方法［J］. 振动、测试与诊断, 2017, 37(2): 339-342.
JIANG Yonghua, LI Rongqiang, JIAO Weidong, et al. Feature extraction method based on empirical mode decomposition and bispectrum analysis［J］. Journal of Vibration, Measurement & Diagnosis, 2017, 37(2):339-342.
［16］GU F, SHAO Y, HU N, et al. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment［J］. Mechanical Systems and Signal Processing, 2011, 25(11):360-372.
［17］陈峙, 王铁, 谷丰收. 基于电动机电流信号双谱分析的齿轮传动故障诊断［J］. 机械工程学报, 2012, 48(21):85-90.
CHEN Zhi, WANG Tie, GU Fengshou. Gear transmission fault diagnosis based on the bispectrum analysis of induction motor current signatures［J］. Journal of Mechanical engineering, 2012, 48(21):85-90.
［18］REHAB I, TIAN X G, GU F S, et al. The fault detection and severity diagnosis of rolling element bearings using modulation signal bispectrum［C］// Eleventh International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. Manchester, UK, 2014.
［19］RAGHUVEER M R. Time-domain approaches to quadratic phase coupling estimation［J］. IEEE Transactions on Automatic Control, 1990, 35(1):48-56.
［20］EBERHART R, KENNEDY J. A new optimizer using particle swarm theory［C］// Proceeding of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan, 1995.
［21］SHI Y H, EBERHART R C. Parameter selection in particle swarm optimization［M］. Berlin-Heidelberg: Springer Berlin Heidelberg, 1998.
［22］ISIET M, GADALA M. Self-adapting control parameters in particle swarm optimization［J］. Applied Soft Computing Journal, 2019,83:105653.
［23］张雄;基于谱峭度类算法的滚动轴承故障特征提取及诊断研究［D］.北京：华北电力大学(北京)，2019. |
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