Comprehensive recognition of rolling bearing fault pattern and fault degrees based on two-layer similarity in phase space
LIU Yongbin1,2,HE Bing1,LIU Fang1,ZHAO Yilei1,FANG Jian1
1.Department of Mechanical Engineering,Anhui University,Hefei 230601,China；
2.Department of Precision Machinery and Precision Instrumentation,University of Science and Technology of China,Hefei 230027,China
Abstract：A comprehensive method for rolling bearing fault patterns and fault degree recognition based on two-layer algorithm structure of phase space similarity analysis was presented in this paper. In the first layer of the algorithm,the data were processed by the phase space reconstruction (PSR) to get a phase space which was equivalence in the topological sense. Then a sliding window was employed to chop the data segments and the normalized cross correlation function (NCC) was employed to execute similarity analysis,realizing the classification of bearing fault patterns. In the second layer,a SVR structure was trained by phase space similarity (PSS) that was obtained in different fault degree. The SVR structure was then used to recognize the fault degree. The results of experimental signal analysis show that the proposed method can effectively recognize comprehensive bearing fault pattern and fault degree. Compared with traditional methods,it shows an improvement in accuracy of recognition.
刘永斌1,2，何兵1，刘方1，赵艺雷1，方健1. 基于双层相空间相似度的滚动轴承故障模式与故障程度的综合辨识[J]. 振动与冲击, 2017, 36(4): 178-184.
LIU Yongbin1,2,HE Bing1,LIU Fang1,ZHAO Yilei1,FANG Jian1. Comprehensive recognition of rolling bearing fault pattern and fault degrees based on two-layer similarity in phase space. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(4): 178-184.
[1]. 何正嘉，訾艳阳，孟庆丰.机械设备非平稳信号的故障诊断原理及应用[M].北京：高等教育出版社,2001.
He Zhengjia, Zi Yanyang, Meng Qingfeng. Mechanical equipment fault diagnosis principle and application of non-stationary signal[M]. Beijing:Higher Education Press,2001.
[2]. 何清波.多元统计分析在设备状态监测诊断中的应用研究[D]. 合肥：中国科学技术大学，2007.
He Qingbo. Research on multivariate statistical analysis for machine condition monitoring and diagnosis[D]. Hefei:University of science and technology of China,2007.
[3].Yang Yu, Yu Denjie, Cheng Jun sheng. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM [J], Measurement, 2007,40: 943-950.
[4]. 孔凡让，朱忠奎，羊拯民，等.齿轮振动信号特征的小波包频率表示法[J].振动、测试与诊断，2004,24(2)：103-105.
Kong Fanrang, Zhu Zhongkui, Yang Zhengmin, et al. The wavelet packet frequency representation of Gear vibration signal characteristics[J]. Journal of Vibration, Measurement & Diagnosis,2004,24(2):103-105.
[5].He Q. Vibration signal classification by wavelet packet energy flow manifold learning [J]. Journal of Sound and Vibration, 2013, 332(7):τ81–1894.
[6]. 刘永斌.基于非线性信号分析的滚动轴承状态监测诊断研究[D].合肥：中国科学技术大学,2011.
Liu Yongbin. Nonlinear signal analysis for rolling bearing condition monitoring and fault diagnosis[D]. Hefei:University of science and technology of China,2011.
[7]. 刘永斌，何清波，孔凡让，等.基于PCA和SVM的内燃机故障诊断[J].振动、测试与诊断，2012,32(2)：250-255.
Liu Yongbin, He Qingbo, Kong Fanrang, et al. The internal combustion engine fault diagnosis based on PCA and SVM[J]. Journal of Vibration, Measurement &Diagnosis,2012,32(2):250-255.
[8]. 朱忠奎，陈祥芹，樊薇，等.基于降维的独立分量分析及信号特征成分监测[J].仪器仪表学报，2014,35(4)，917-923.
Zhu Zhongkui, Cheng Xiangqing, Fan Wei, et al. Independent component analysis based on dimensionality reduction and its application in signal feature detection[J]. Chinese Journal of Scientific Instrument, 2014, 35(4), 917-923.
[9]. Packard N H, Crutchfield J P, Farmer J D, et al. Geometry from a time series[J]. Physical review letters, 1980, 43(9):712-715.
[10].陈铿，韩伯棠.混沌时间序列分析中的相空间重构技术综述[J].计算机科学，2005,32(4):67-70.
Chen Keng, Han Botang. A survey of state space reconsition of chaotic time serious analysis[J]. Chinese Journal of Computer ,2005,32(4):67-70.
[11].孙雅明，张智晟．相空间重构和混沌神经网络融合的短期负荷预测研究[J]．中国电机工程学报,2004, 24(1)：44-48．
Sun Yaming，Zhang Zhisheng．A new model of STLF based on the fusion of PSRT and chaotic neural networks[J]．Proceedings of the CSEE，2004，24(1):44-48．
[12]. Rosenstein M T, Collins J J, De Luca C J. A practical method for calculating largest Lyapunov exponents from small data sets[J]. Physica D,1993,65:117-134.
[13].Kermel M B, Brown R, Abarbanel H D I. Detennining embedding dimension for phase-space reconstruction using a geometrical reconstruction [J].PhysRevA,1992,45:3403-3410.
[14].Cao L Y. Practical method for determining the minimum embedding dimension of a scalar time series [J]. Physica D, 1997,110:43-50.
[15]. Brown L G. A survey of image registration techniques[J]. ACM Computing Surveys, 1992: 325-376.
[16].Tsai D M, Lin C T. Fast normalized cross correlation for defect detection[J]. Pattern Recognition Letter, 2003,24:2625-2631.
[17].Vapnik V N. The Nature of Statistical Learning Theory[M]. New York Spring-Verlag 1995.
[18].沈长青，朱忠奎，黄伟国，等.基于支持向量回归方法的齿轮箱故障诊断研究[J]，振动、测试与诊断，2013,33（5）：775-781.
Shen Changqing, Zhu Zhongkui, Huang Weiguo, et al. Gear box fault diagnosis based on support vector regression method[J]. Journal of Vibration, Measurement &Diagnosis,2013,33(5):775-781.
[19].胥永刚，何正嘉. 分形维数和近似熵用于度量信号复杂性的比较研究[J]，振动与冲击，2003，22（3）.
Xu Yonggang, He zhengjia. Research on comparison between approximate entropy and fractal dimension for complexity of signals [J], Journal of Vibration And Shock, 2003,22(3).
