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

[31] Data-Driven Fault Detection in Reciprocating Compressors: A Method Based on PCA and GLRT [J].

Cabrera, Mauricio ;

Cabrera, Diego ;

Cerrada, Mariela ;

Sanchez, Rene-Vinicio .

IFAC PAPERSONLINE, 2024, 58 (08) :264-269

[32] An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis [J].

Guo, Yabin ;

Li, Guannan ;

Chen, Huanxin ;

Hu, Yunpeng ;

Li, Haorong ;

Xing, Lu ;

Hu, Wenju .

ENERGY AND BUILDINGS, 2017, 142 :167-178

[33] A Novel Fault Detection Method for Semiconductor Manufacturing Processes [J].

Sun, Zhen ;

Yang, Jingli ;

Zheng, Kexin .

2019 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC), 2019, :1469-1474

[34] Fault detection based on weighted difference principal component analysis [J].

Guo, Jinyu ;

Wang, Xin ;

Li, Yuan ;

Wang, Guozhu .

JOURNAL OF CHEMOMETRICS, 2017, 31 (11)

[35] A new adaptive PCA based thresholding scheme for fault detection in complex systems [J].

Bakdi, Azzeddine ;

Kouadri, Abdelmalek .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2017, 162 :83-93

[36] Fault Detection and Data Restoration Based on PCA for Sensors of Autonomous Underwater Vehicle [J].

Wang, Yujia ;

Zhang, Mingjun ;

Guo, Yong .

2009 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS 1-7, CONFERENCE PROCEEDINGS, 2009, :4801-4805

[37] Sensor Fault Detection in Uncertain Large-Scale Systems Using Interval-Valued PCA Technique [J].

Louifi, Abdelhalim ;

Kouadri, Abdelmalek ;

Harkat, Mohamed Faouzi ;

Bensmail, Abderazak ;

Mansouri, Majdi .

IEEE SENSORS JOURNAL, 2025, 25 (02) :3119-3125

[38] The impact of improved PCA method based on anomaly detection on chiller sensor fault detection [J].

Liang, Aosong ;

Hu, Yunpeng ;

Li, Guannan .

INTERNATIONAL JOURNAL OF REFRIGERATION, 2023, 155 :184-194

[39] An enhanced PCA-based chiller sensor fault detection method using ensemble empirical mode decomposition based denoising [J].

Li, Guannan ;

Hu, Yunpeng .

ENERGY AND BUILDINGS, 2019, 183 :311-324

[40] A novel fault detection approach based on multilinear sparse PCA: application onthe semiconductor manufacturing processes [J].

Toumi, Riadh ;

Kourd, Yahia ;

Lefebvre, Dimitri .

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2022, 30 (04) :1586-1599

← 1 2 3 4 5 →