Fault detection method based on minimum sufficient statistics pattern analysis

被引：0

作者：

Sun S. ^{[1
]}

Dong S. ^{[2
]}

Jiang Y. ^{[3
]}

Zhou T. ^{[3
]}

Li Y. ^{[2
]}

机构：

[1] Jiangsu Frontier Electric Technology Co. Ltd., Nanjing, 211102, Jiangsu

[2] School of Energy and Environment, Southeast University, Nanjing, 210096, Jiangsu

[3] Jiangsu Electric Power Co. Ltd., Nanjing, 210024, Jiangsu

来源：

Huagong Xuebao/CIESC Journal | 2018年 / 69卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Algorithm; Fault detection; Minimum sufficient statistics; Principal component analysis; Process systems; Statistics pattern analysis;

D O I：

10.11949/j.issn.0438-1157.20171054

中图分类号：

学科分类号：

摘要：

Recently, the statistic pattern analysis (SPA) has been used with widespread applications in the field of fault detection. Its essence is to use data statistics matrix for process monitoring instead of the original data matrix. However, SPA lacks reasonable method in choosing the statistics variables, also complex nonlinear interactions exist among these statistics variables. As a result, fault detection cannot be processed by using ordinary principal component analysis (PCA) algorithm. In order to solve these problems, a new minimum sufficient statistics pattern analysis (MSSPA) fault detection method is proposed. This method first eliminates the correlations among variables by performing an orthogonal transformation of the raw data matrix, and then estimates the probability density function of the single variables or joint probability density function of multiple variables, so as to acquire the minimum sufficient statistic of original data, and construct the statistic matrix with it. The introduction of minimum sufficient statistics is also beneficial to handle the problem of non-Gaussian distribution of the raw data. Finally, the feasibility and validity of this method for fault detection are verified by testing on the Tennessee Eastmann (TE) process. © All Right Reserved.

引用

页码：1228 / 1237

页数：9

共 30 条

[1]

Ge Z., Song Z., Gao F., Review of recent research on data-based process monitoring, Industrial & Engineering Chemistry Research, 52, 10, pp. 3543-3562, (2013)

[2]

Russell E.L., Chiang L.H., Braatz R.D., Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis, Chemometrics & Intelligent Laboratory Systems, 51, 1, pp. 81-93, (2000)

[3]

Sang W.C., Lee C., Lee J.M., Et al., Fault detection and identification of nonlinear processes based on kernel PCA, Chemometrics & Intelligent Laboratory Systems, 75, 1, pp. 55-67, (2005)

[4]

Li W., Yue H.H., Valle-Cervantes S., Et al., Recursive PCA for adaptive process monitoring, Journal of Process Control, 10, 5, pp. 471-486, (2000)

[5]

Ge Z., Zhang M., Song Z., Nonlinear process monitoring based on linear subspace and Bayesian inference, Journal of Process Control, 20, 5, pp. 676-688, (2010)

[6]

He Q.P., Wang J., Statistics pattern analysis: a new process monitoring framework and its application to semiconductor batch processes, AIChE Journal, 57, 1, pp. 107-121, (2015)

[7]

Wang J., He Q.P., Multivariate statistical process monitoring based on statistics pattern analysis, Industrial & Engineering Chemistry Research, 49, 17, pp. 7858-7869, (2010)

[8]

Chang P., Wang P., Gao X.J., Et al., Batch process monitoring and quality prediction based on statistics pattern analysis and MKPLS, Chinese Journal of Scientific Instrument, 35, 6, pp. 1409-1416, (2014)

[9]

Chang P., Wang P., Gao X.J., Fault monitoring batch process based on statistics pattern analysis of T-KPLS, CIESC Journal, 66, 1, pp. 265-271, (2015)

[10]

Zhang H.Y., Tian X.M., Deng X.G., Fault identification method based on SPA similarity factor, CIESC Journal, 64, 12, pp. 4503-4508, (2013)

← 1 2 3 →