A Novel Decentralized Weighted ReliefF-PCA Method for Fault Detection

被引：5

作者：

Yang, Yinghua ^{[1
]}

Chen, Xiangming ^{[1
]}

Zhang, Yue ^{[1
]}

Liu, Xiaozhi ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China

来源：

IEEE ACCESS | 2019年 / 7卷

关键词：

Principal component analysis; Fault detection; Monitoring; Classification algorithms; Covariance matrices; Numerical models; Correlation; ReliefF algorithm; decentralized weighted model; principal component analysis; Bayesian information criterion; DIAGNOSIS; RECONSTRUCTION;

D O I：

10.1109/ACCESS.2019.2943024

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The decentralized weighted ReliefF-PCA (DWRPCA) method is proposed to improve the performance of principal component analysis (PCA) for fault detection. The improved ReliefF-PCA algorithm is used to select the principal components instead of the traditional cumulative percent variance (CPV) criterion, so that the important information contained in the small variance is considered. The sub-models for different types of faults which are being considered the influence weights of process variables and faults are established respectively to obtain the decentralized weighted model. The Bayesian Information Criterion is adopted to integrate different types of faults for a unified monitoring index. The case study of a numerical example and the Tennessee Eastman process illustrate the effectiveness of the proposed method.

引用

页码：140478 / 140487

页数：10

共 50 条

[21] Decentralized Fault Diagnosis of Continuous Annealing Processes Based on Multilevel PCA [J].

Liu, Qiang ;

Qin, S. Joe ;

Chai, Tianyou .

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2013, 10 (03) :687-698

[22] Comments on the applicability of "An improved weighted recursive PCA algorithm for adaptive fault detection" [J].

Quinones-Grueiro, Marcos ;

Verde, Cristina .

CONTROL ENGINEERING PRACTICE, 2017, 58 :254-255

[23] An improved weighted recursive PCA algorithm for adaptive fault detection [J].

Portnoy, Ivan ;

Melendez, Kevin ;

Pinzon, Horacio ;

Sanjuan, Marco .

CONTROL ENGINEERING PRACTICE, 2016, 50 :69-83

[24] Sparse PCA Support Exploration of Process Structures for Decentralized Fault Detection [J].

Theisen, Maximilian ;

Dorgo, Gyula ;

Abonyi, Janos ;

Palazoglu, Ahmet .

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2021, 60 (22) :8183-8195

[25] Decentralized adaptively weighted stacked autoencoder-based incipient fault detection for nonlinear industrial processes [J].

Gao, Huihui ;

Huang, Wenjie ;

Gao, Xuejin ;

Han, Honggui .

ISA TRANSACTIONS, 2023, 139 :216-228

[26] Double-weighted neighborhood standardization method with applications to multimode-process fault detection [J].

Xie, Ying .

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (01) :1243-1256

[27] An Improved PCA-based Fault Detection Method for non-Gaussian Systems Using SIP Criterion [J].

Ren, Mi F. ;

Liang, Yan ;

Gong, Ming Y. .

2019 IEEE 15TH INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA), 2019, :905-910

[28] An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm [J].

Li, Guannan ;

Hu, Yunpeng ;

Chen, Huanxin ;

Shen, Limei ;

Li, Haorong ;

Hu, Min ;

Liu, Jiangyan ;

Sun, Kaizheng .

ENERGY AND BUILDINGS, 2016, 116 :104-113

[29] Comparative study of PCA approaches for fault detection: Application to a chemical reactor [J].

Jaffel, Ines ;

Taouali, Okba ;

Elaissi, Ilyes ;

Messaoud, Hassani .

2013 INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT), 2013, :57-62

[30] PCA-based method of soft fault detection and identification for the ongoing commissioning of chillers [J].

Cotrufo, Nunzio ;

Zmeureanu, Radu .

ENERGY AND BUILDINGS, 2016, 130 :443-452

← 1 2 3 4 5 →