k-Nearest Neighbor Imputation Method and Its Application in Fault Diagnosis of Industrial Process

Abstract

Abstract:

Abstract： Aimed at the smearing effect in contribution plot method and the falt that fault variables cannot be located, this paper proposed a kNN imputation method for fault diagnosis, combining k-nearest neighbor and the contribution plot algorithm. First, PCA was adopted to build an evaluation model and calculate the combined index. Secondly, knearest neighbor imputation method and the control index were combined to extract preliminary faulty variables. Finally, the contribution plots were employed to find the fundamental faulty variables from the preliminary faults. The proposed method can avoid the influence of contribution values of normal variables effectively. A numerical example and Tennessee Eastman (TE) process were given to verify the effectiveness and accuracy of the proposed method, compared with the reconstruction-based method.

Key words: k-nearest neighbor(kNN), reconstruction-based contribution(RBC), contribution plot, fault diagnosis

CLC Number:

TP 274

LI Yuan1,WU Jie1,WANG Guozhu2. k-Nearest Neighbor Imputation Method and Its Application in Fault Diagnosis of Industrial Process[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 830-836.

References

［1］Wise B M, Ricker N L, Veltkamp D F, et al. A theoretical basis for the use of principal component models for monitoring multivariate processes［J］. Process Control and Quality,1990(1):4151.

［2］S. Joe Qin. Statistical process monitoring: basics and beyond［J］. Journal of Chemometrics,2003,17:480502.

［3］Hopkins R W, Miller P, Scheible J J, et al. Method of controlling a manufacturing process using multivariate analysis: U S. Patent 5,442,562［P］.1995815.

［4］Alcala C F, Qin S J. Reconstructionbased contribution for process monitoring［J］. Automatica,2009,45(7):15931600.

［5］Dunia R, Joe Qin S. Subspace approach to multidimensional fault identification and reconstruction［J］. AIChE Journal, 1998,44(8):18131831.

［6］Yue H H, Qin S J. Reconstructionbased fault identification using a combined index［J］. Industrial & Engineering Chemistry Research,2001,40(20):44034414.

［7］Alcala C F, S. Joe Qin. Analysis and generalization of fault diagnosis methods for process monitoring［J］. Journal of Process Control, 2011,21(3):322330.

［8］周东华,李钢,李元.数据驱动的过程故障检测与诊断技术［M］.北京：科学出版社,2011.

［9］Guozhu Wang, Jianchang Liu, Yuan Li. A progressive fault detection and diagnosis method based on dissimilarity of process data［C］∥Proceeding of the IEEE International Conference on Information and Automation. Hailar,China,July 2014,12111216.

［10］Zhao C, Sun Y, Gao F. A multipletimeregion (MTR)based fault subspace decomposition and reconstruction modeling strategy for online fault diagnosis［J］. Industrial & Engineering Chemistry Research,2012,51(34):1120711217.

［11］Cover T, Hart P. Nearest neighbor pattern classification［J］. Information Theory, IEEE Transactions on,1967, 13(1):2127.

［12］He Q P, Wang J. Fault detection using the knearest neighbor rule for semiconductor manufacturing processes［J］. Semiconductor Manufacturing, IEEE Transactions on,2007,20(4):345354.

［13］CHEN H, GUO J, XIE Y. kNN Fault Detection Based on Improved Kmeans Clustering［J］. Journal of Shenyang University of Chemical Technology,2013,27(1):6973.

［14］Li Y, Zhang X. Diffusion maps based knearestneighbor rule technique for semiconductor manufacturing process fault detection［J］. Chemometrics and Intelligent Laboratory Systems,2014,136:4757.

［15］Wang Q, Liu Y B, He X, et al. Fault diagnosis of bearing based on KPCA and KNN method［J］. Advanced Materials Research,2014,986:14911496.

［16］Downs J J, Vogel E F. A plantwide industrial process control problem［J］. Computers & Chemical Engineering,1993,17(3):245255.

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