Small Fault Detection Based on Cumulative Sum of Neighbor Statistic

doi:10.16182/j.issn1004731x.joss.19-0663

Abstract

Abstract: Aiming at the small faults and the common data non-linear problems of industrial process, a fault detection method based oncumulative sum of neighbor statistic (CUSUM-NS) is proposed. Mutual information principal component analysis (MIPCA) is used to reduce the dimension of training data, and the principal components based on mutual information are extracted to construct a new sample space. For the new sample space after dimensionality reduction, the nonlinear features of the process data can be fully extracted through the distance square sum statistics of k nearest neighbors. Cumulative summation(CUSUM) method is used to accumulate the sum of squares of neighboring distances to capture the small changes in process data. The simulation is conducted through the Tennessee Eastman (TE) process, and the results verify the effectiveness of the proposed method.

Key words: PCA based on mutual information, k-Nearest Neighbor, cumulative sum, small faults

CLC Number:

TP277

Guo Xiaoping, Gao Jiajun, Guo Jianbin, Li Yuan. Small Fault Detection Based on Cumulative Sum of Neighbor Statistic[J]. Journal of System Simulation, 2021, 33(4): 792-800.

References

[1] 彭开香, 马亮, 张凯. 复杂工业过程质量相关的故障检测与诊断技术综述[J]. 自动化学报, 2017, 43(3): 349-365.
Peng Kaixiang, Ma Liang, Zhang Kai.Review of Quality-related Fault Detection and Diagnosis Techniques for Complex Industrial Processes[J]. Acta Automatica Sinica, 2017, 43(3): 349-365.
[2] 郑茜予. 基于主元分析的微小故障检测[D]. 北京: 华北电力大学, 2015.
Zheng Xiyu.Incipient Fault Detection Based on Principal Component Analysis[D]. Beijing: North China Electric Power University, 2015.
[3] Zhang S, Zhao C.Stationarity Test and Bayesian Monitoring Strategy for Fault Detection in Nonlinear Multimode Processes[J]. Chemometrics and Intelligent Laboratory Systems(S0169-7439), 2017, 168(15): 45-61.
[4] Ge Z.Review on Data-driven Modeling and Monitoring for Plant-wide Industrial Processes[J]. Chemometrics and Intelligent Laboratory Systems(S0169-7439), 2017, 171(15): 16-25.
[5] 文成林, 吕菲亚, 包哲静, 等. 基于数据驱动的微小故障诊断方法综述[J]. 自动化学报, 2016, 42(9): 1285-1299.
Wen Chenglin, Lü Feiya, Bao Zhejing, et al.A Review of Data Driven-based Incipient Fault Diagnosis[J].Acta Automatica Sinica, 2016, 42(9): 1285-1299.
[6] Zhu J, Ge Z, Song Z.Distributed Parallel PCA for Modeling and Monitoring of Large-scale Plant-wide Processes with Big Data[J]. IEEE Transactions on Industrial Informatics (S1551-3203), 2017, 13(4): 1877-1885.
[7] 刘舒锐, 彭慧, 李帅, 等. 基于IJB-PCA-ICA算法的故障检测[J].化工学报, 2018, 69(12): 227-235.
Liu Shurui, Peng Hui, Li Shuai, et al.Fault Detection based on IJB-PCA-ICA[J].Journal of Chemical Industry and Engineering, 2018, 69(12): 227-235.
[8] 郭小萍, 袁杰, 李元. 基于核特征近邻指标的批过程监视及仿真研究[J]. 系统仿真学报, 2014, 26(7): 1424-1429.
Guo Xiaoping, Yuan Jie, Li Yuan.The Nearest Neighbors Index of Kernel Features Based Batch Processes Monitoring and Simulation Research[J]. Journal of System Simulation, 2014, 26(7): 1424-1429.
[9] He Q P, Wang J.Fault Detection Using the k-nearest Neighbor Rule for Semiconductor Manufacturing Processes[J]. IEEE Transactions on Semiconductor Manufacturing (S0894-6507), 2007, 20(4): 345-354.
[10] 张成, 李元. PC-KNN故障检测方法在半导体批次过程中的应用研究[C]//中国控制与决策会议. 沈阳: 东北大学出版社, 2013: 4209-4214.
Zhang Cheng, Li Yuan.The Application and Research of Fault Detection Based on PC-KNN in Semiconductor Batch Process[C]//Chinese Control and Decision Conference. Shenyang: Northeast University Press, 2013: 4209-4214.
[11] Wold S.Exponentially Weighted Moving Principal Components Analysis and Projections to Latent Structures[J]. Chemometrics and Intelligent Laboratory Systems (S0169-7439), 1994, 23(1): 149-161.
[12] Du Y, Du D.Fault Detection and Diagnosis Using Empirical Mode Decomposition based Principal Component Analysis[J]. Computers & Chemical Engineering (S0098-1354), 2018, 115(12): 1-21.
[13] Shams M A B, Budman H M, Duever T A. Fault Detection, Identification and Diagnosis Using CUSUM based PCA[J]. Chemical Engineering Science (S0009-2509), 2011, 66(20): 4488-4498.
[14] 葛志强, 宋执环, 杨春节. 基于MCUSUM-ICA-PCA 的微小故障检测[J]. 浙江大学学报(工学版), 2008, 42(3): 373-377.
Ge Zhiqiang, Song Zhihuan, Yang Chunjie.Small Shift Detection based on MCUSUM-ICA-PCA[J]. Zhejiang University of Technology (Engineering Edition), 2008, 42(3): 373-377.
[15] 范雪莉, 冯海泓, 原猛. 基于互信息的主元分析特征选择算法[J]. 控制与决策, 2013 (6): 915-919.
Fan Xueli, Feng Haihong, Yuan Meng.PCA based on Mutual Information for Feature Selection[J]. Control and Decision, 2013 (6): 915-919.
[16] 童楚东, 史旭华. 基于互信息的PCA 方法及其在过程监测中的应用[J]. 化工学报, 2015, 66(10): 4101-4106.
Tong Chudong, Shi Xuhua.Mutual Information based PCA Algorithm with Application in Process Monitoring[J]. Journal of Chemical Industry and Engineering, 2015, 66(10): 4101-4106.
[17] 童楚东, 蓝艇, 史旭华. 基于互信息的分散式动态PCA 故障检测方法[J]. 化工学报, 2016, 67(10): 4317-4323.
Tong Chudong, Lan Ting, Shi Xuhua.Fault Detection by Decentralized Dynamic PCA Algorithm on Mutual Information[J]. Journal of Chemical Industry and Engineering, 2016, 67(10): 4317-4323.
[18] 王敏华, 周娟, 沈丹. 累积和控制图与休哈特控制图联合应用方法[J]. 湖北工业大学学报, 2008, 23(5): 59-61.
Wang Minhua, Zhou Juan, Shen Dan.A Research on the Method of Integrated Usage of CUSUM Chart and Shewhart Control Chart[J]. Journal of Hubei University of Technology, 2008, 23(5): 59-61.
[19] Wang W, Wells M T.Model Selection and Semiparametric Inference for Bivariate Failure-time Data[J]. Journal of the American Statistical Association (S0162-1459), 2000, 95(449): 62-72.
[20] Kano M, Nagao K, Hasebe S, et al.Comparison of Multivariate Statistical Process Monitoring Methods with Applications to the Eastman Challenge Problem[J]. Computers & Chemical Engineering (S0098-1354), 2002, 26(2): 161-174.
[21] Molina G D, Zumoffen D A R, Basualdo M S. Plant-wide Control Strategy Applied to the Tennessee Eastman Process at two Operating Points[J]. Computers & Chemical Engineering (S0098-1354), 2011, 35(10): 2081-2097.
[22] 韩晓春, 薄翠梅, 易辉. 基于DNPE-SVDD的化工过程监控[J]. 系统仿真学报, 2018, 30(1): 184-190.
Han Xiaochun, Bo Cuimei, Yi Hui.Chemical Process Monitoring Based on DNPE-SVDD[J]. Journal of System Simulation, 2018, 30(1): 184-190.
[23] Wang J, He Q P.Multivariate Statistical Process Monitoring based on Statistics Pattern Analysis[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2010, 49(17): 7858-7869.
[24] Tong C, Song Y, Yan X.Distributed Statistical Process Monitoring based on Four-subspace Construction and Bayesian Inference[J]. Industrial & Engineering Chemistry Research (S0888-5885), 2013, 52(29): 9897-9907.
[25] 邓晓刚, 邓佳伟, 曹玉苹, 等. 基于双层局部 KPCA 的非线性过程微小故障检测方法[J]. 化工学报, 2018 69(7): 3092-3100.
Deng Xiaogang, Deng Jiawei, Cao Yuping, et al.Incipient Fault Detection for Nonlinear Process based on Double-level Local KPCA[J]. Journal of Chemical Industry and Engineering, 2018, 69(7): 3092-3100.