Study on Evaluation of Incipient Performance Degradation of Rolling Bearings
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摘要: 为了解决轴承早期性能退化时信噪比低,特征提取和早期性能退化评估困难这一难题,本文采用盲源分离的方法分离轴承振动信号的干扰,将盲源分离后轴承振动信号的峭度值作为轴承性能评估的敏感特征,利用动态模糊神经网络建立轴承的早期性能退化模型。根据盲源分离后,早期性能退化时轴承振动信号的峭度值增加,可作为轴承早期性能退化评估的敏感特征。计算结果表明,盲源分离使得振动信号的峭度对轴承的性能状态更加敏感,轴承性能退化评估结果准确,具有重要的工业实用价值。Abstract: Signal-to-noise rates(SNR) is very poor when incipient faults occur on rolling bearings, which makes it difficult to extract fault features and evaluate performance degradation of rolling bearings. Blind source separation(BSS) method is adapted to separate noise impulses mixed in measured signals of rolling bearings. Kurtosis values are sensitive features when measured signals are separated by BSS method. A performance evaluating model of incipient degradation of rolling bearings is built based on dynamic fuzzy neutral network(DFNN) and kurtosis values are input vectors of the evaluating model. The results of calculation show that the kurtosis is more sensitive to the performance and evaluation results of degradation performance of rolling bearings are more accurate. The method of performance degradation based on kurtosis and BSS is significant to solve engineering problems.
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[1] Papaelias M, Amini A, Huang Z, et al. Online condition monitoring of rolling stock wheels and axle bearings[J]. Proceedings of the Institution of Mechanical Engineers, Part F:Journal of Rail and Rapid Transit, 2014,230(3):709-723 [2] Tiwari R, Gupta V K, Kankar P K. Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier[J]. Journal of Vibration and Control, 2015,21(3):461-467 [3] 张晗,杜朝辉,方作为,等.基于稀疏分解理论的航空发动机轴承故障诊断[J].机械工程学报,2015,51(1):97-105 Zhang H, Du Z H, Fang Z W, et al. Sparse decomposition based aero-engine's bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2015,51(1):97-105(in Chinese) [4] 马新娜,杨绍普.滚动轴承复合故障诊断的自适应方法研究[J].振动与冲击,2016,35(10):145-150 Ma X N, Yang S P. Adaptive compound fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2016,35(10):145-150(in Chinese) [5] 郑红,周雷,杨浩.基于小波包分析与多核学习的滚动轴 承故障诊断[J].航空动力学报,2015,30(12):3035-3042 Zheng H, Zhou L, Yang H. Rolling bearing fault diagnosis based on wavelet packet analysis and multi kernel learning[J]. Journal of Aerospace Power, 2015,30(12):3035-3042(in Chinese) [6] Ali, J B, Chebel-Morello B, Saidi L, et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J]. Mechanical Systems and Signal Processing, 2015,56-57:150-172 [7] Wang Y X, 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 [8] 张念,刘天佑,李杰.FastICA算法及其在地震信号去噪中的应用[J].计算机应用研究,2009,26(4):1432-1434 Zhang N, Liu T Y, Li J. FastICA algorithm and its application in seismic signal noise elimination[J]. Application Research of Computers, 2009,26(4):1432-1434(in Chinese) [9] 钟丽莉,熊兴中.基于峭度的独立分量算法的性能分析研究[J].四川理工学院学报(自然科学版),2014,27(4):43-47 Zhong L L, Xiong X Z. Research on performance analysis of independent component algorithm based on kurtosis[J]. Journal of Sichuan University of Science & Engineering(Natural Science Edition), 2014,27(4):43-47(in Chinese) [10] 伍世虔,徐军.动态模糊神经网络-设计与应用[M].北京:清华大学出版社,2008:27-38 Wu S Y, Xu J. Dynamic fuzzy neurol network-design and application[M]. Beijing:Tsinghua University Press, 2008:27-38(in Chinese)
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