The Application in Classification method of Rotor Fault Data Set Using Correlation Manifold Distance

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (18) : 125-130.

Zhao Rongzhen Zhao XiaoLi He Jingju Liu Yunjia

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Abstract

Aiming at the problem of accurate classification is difficult which is due to a certain correlation between fault feature attribute domain, a kind of rotor fault data classification method of considering the influence of the correlation coefficient is proposed. This method is the result of concept combining the Correlation Manifold Distance Marginal Fisher Analysis (Correlation Manifold Distance Marginal Fisher Analysis, CDMFA) and Correlation Manifold Distance K - Nearest Neighbor (Correlation Manifold Distance K-Nearest Neighbor, CDKNN) classifier together. First of all, the vibration signal are converted into high-dimensional data-set of multi-domain and multi-channel. Then, using correlation manifold distance of the fused correlation coefficient to measure neighbors and weights of fault samples by the CDMFA, which can better reflect the similarity relation between high-dimensional data and extract low-dimensional feature subset of making the bigger distance between the class. Finally, the low-dimensional feature subset is input into CDKNN classifier for fault pattern recognition. The proposed method is verified by using a double span rotor system data-set and simulation data-set. The results show that the method has better dimension reduction effect and higher fault classification accuracy. The study finds that the manifold distance fault data classification method can reveal more realistic high-dimensional feature geometry relation. This method provides the theory reference of data preprocessing for feature attribute reduction and classification of the high dimensional fault data-set.

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Fault Classification / Correlation Manifold Distance / Marginal Fisher Analysis (MFA) / K-nearest neighbor (KNN) classifier / Rotor Fault Dataset

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Zhao Rongzhen Zhao XiaoLi He Jingju Liu Yunjia. The Application in Classification method of Rotor Fault Data Set Using Correlation Manifold Distance[J]. Journal of Vibration and Shock, 2017, 36(18): 125-130

References

[1] Lei Y, He Z, Zi Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery[J]. Mechanical Systems & Signal Processing, 2009, 23(4): 1327-1338.
[2] 艾延廷，费成巍，王志. 基于改进模糊SVM的转子振动故障诊断技术[J]. 航空动力学报，2011, 26(5): 1118-1123.
AI Yanting, FEI Chengwei, WANG Zhi. Technique for diagnosing fault of rotor vibration based on improved fuzzy SVM[J]. Journal of Aerospace Power, 2011, 26(5): 1118-1123.
[3] Lei Y, He Z, Zi Y. A new approach to intelligent fault diagnosis of rotating machinery[J]. Expert Systems with Applications, 2008, 35(4):1593-1600.
[4] Su Z, Tang B, Ma J, et al. Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier[J]. Measurement, 2014, 48(1):136-148.
[5] Jiang Q, Jia M, Hu J, et al. Machinery fault diagnosis using supervised manifold learning[J]. Mechanical Systems & Signal Processing, 2009, 23(7): 2301-2311.
[6] Yan S, Xu D, Zhang B, et al. Graph Embedding and Extension: A General Framework for Dimensionality Reduction [J]. Pattern Analysis & Machine Intelligence IEEE Transactions on, 2007, 29(1):40-51.
[7] Jiang L, Shi T, Xuan J. Fault diagnosis of rolling bearings based on Marginal Fisher analysis[J]. Journal of Vibration & Control, 2014, 20(3): 470-480.
[8] Xu D, Yan S, Tao D, et al. Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval.[J]. IEEE Transactions on Image Processing, 2007, 16(11): 2811-21.
[9] 史骏，陈才扣. 基于马氏距离的半监督鉴别分析及人脸识别[J]. 北京航空航天大学学报，2011, 37(12):1589-1593. Shi Jun, Chen Caikou. Mahalanobis distance-based semi-supervised discriminant analysis for face recognition [J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(12): 1589-1593.
[10] 刘嘉敏，罗甫林，黄鸿，等. 应用相关近邻局部线性嵌入算法的高光谱遥感影像分类[J]. 光学精密工程，2014, 22(6): 1668-1676.
LIU J M, LUO F L, HUANG H, et al. Classification of hyperspectral　remote sensing images using　correlation neighbor LEE.[J] Optics and Precision Engineering, 22(6):1668-1676.
[11] Falie D, David L. Correlation coefficient based on independent component analysis[C]// International Conference on Communications. 2012: 59-62.
[12] Blake, C.L. and Merz, C.J., 1998, UCI repository of machine learning databases. University of California. Available online at: http://archive.ics.uci.edu/ml/
[13] 霍天龙，赵荣珍，胡宝权. 基于熵带法与PSO优化的SVM转子故障诊断[J]. 振动、测试与诊断，2011, 31(3): 279-284.
Huo Tianlong, Zhao Rongzhen, Hu Baoquan. Fault diagnosis for rotor systems based on entropy Band method and support vector machine optimized by PSO[J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(3): 279-284.
[14] 王泽杰，胡浩民. 流形学习算法中的参数选择问题研究[J]. 计算机应用与软件，2010, 27(06): 84-85.
WANG Ze-jie, HU Hao-min. On parameter selection in manifold learning algorithm[J]. Computer Applications and Software，2010，27 ( 6):84 -85．
[15] Chen F, Tang B, Chen R. A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm[J]. Measurement, 2013, 46(1): 220-232.