Fault Diagnosis Algorithm of Based Feature Subspace Estimation in Small Sample Circumstance

Abstract

Abstract:

Abstract： To solve the problem that a robust covariance matrix cannot be obtained because of insufficient samples in principal component analysis, the covariance matrix was transformed into the feature subspace estimation problem by introducing the idea of CS decomposition Bayesian spatial estimation. First, the SPE (squard prediction error) statistic threshold and failure mode feature subspace matrix library were established using a large number of historical data using PCA offline. When there exists an abnormal condition in the online system, only a small sample of failure data can be obtained due to the effect of a certain environment. However, the feature subspace matrix can be obtained using a small sample data. Then, fault diagnosis was completed by comparing the similarity between the feature subspace and the failure mode subspace. Finally, the feasibility and effectiveness of this method was verified by simulation.

Key words: principal component analysis (PCA), small sample, fault diagnosis, similarity of the matrix

CLC Number:

TP 273.5

HOU Yandong,YAN Zhiyu,JIN Yong. Fault Diagnosis Algorithm of Based Feature Subspace Estimation in Small Sample Circumstance[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 825-829.

References

［1］周东华, 叶银忠. 现代故障诊断与容错控制［M］. 北京：清华大学出版社, 2000.

［2］Ding S X. Datadriven design of fault diagnosis and faulttolerant control systems［M］. London: Springer, 2014.

［3］杜海莲, 王占锋, 吕锋, 等. 基于主元分析的多传感器故障检测［J］. 南京航空航天大学学报, 2011, 43(B07): 138141.

DU Hailian, WANG Zhanfeng, L Feng, et al. Fault detection for multisensor based on PCA［J］. Journal of Nanjing University of Aeronautics & Astronautics, 2011, 43(B07): 138141.

［4］Zhang J, Martin E B, Morris A J. Fault detection and diagnosis using multivariate statistical techniques: Process operations and control［J］. Chemical Engineering Research & Design, 1996, 74(1): 8996.

［5］郭明, 王树青. 基于特征子空间的系统性能监控与工况识别［J］. 化工学报, 2004, 55(1): 151154.

GUO Ming, WANG Shuqing. Systee performance monitoring and region identification based on characteristic subspace［J］. Journal of Chemical Industry and Engineering (China), 2004, 55(1): 151154.

［6］Besson O, Dobigeon N, Tourneret J Y. CS decomposition based Bayesian subspace estimation［J］. Signal Processing, IEEE Transactions on, 2012, 60(8): 42104218.

［7］Jackson J E. A user’s guide to principal components［M］. New York: WileyInterscience, 1991.

［8］Besson O, Dobigeon N, Tourneret J Y. Minimum mean square distance estimation of a subspace［J］. Signal Processing, IEEE Transactions on, 2011, 59(12): 57095720.

［9］Golub G H, Van Loan C F. Matrix computations ［M］. Baltimore: Johns Hopkins University Press, 2012.

［10］王姝. 基于数据的间歇过程故障诊断及预测方法研究［D］. 沈阳：东北大学, 2010.

［11］Tadjudin S, Landgrebe D A. Covariance estimation for limited training samples［C］∥IEEE Geoscience and Remote Sensing Symposium Proceedings. Seattle: IEEE International， 1998: 26882690.

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