New sensor fault detection and isolation strategy-based interval-valued data

被引:11
作者
Harkat, Mohamed Faouzi [1 ]
Mansouri, Majdi [2 ]
Abodayeh, Kamaleldin [3 ]
Nounou, Mohamed [4 ]
Nounou, Hazem [2 ]
机构
[1] Badji Mokhtar Annaba Univ, Lab Mat Avances, POB 12, Annaba 23000, Algeria
[2] Texas A&M Univ Qatar, Elect & Comp Engn Program, Doha, Qatar
[3] Prince Sultan Univ, Dept Math Sci, Riyadh, Saudi Arabia
[4] Texas A&M Univ Qatar, Chem Engn Program, Doha, Qatar
关键词
data-driven process monitoring; fault detection and isolation; generalized likelihood ratio; interval-valued data; principal component analysis; reconstruction; PRINCIPAL COMPONENT ANALYSIS; PLS-BASED GLRT; RECONSTRUCTION; IDENTIFICATION; DIAGNOSIS; SELECTION; NUMBER; FDI; PCA;
D O I
10.1002/cem.3222
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a new data-driven sensor fault detection and isolation (FDI) technique for interval-valued data is developed. The developed approach merges the benefits of generalized likelihood ratio (GLR) with interval-valued data and principal component analysis (PCA). This paper has three main contributions. The first contribution is to develop a criterion based on the variance of interval-valued reconstruction error to select the number of principal components to be kept in the PCA model. Secondly, interval-valued residuals are generated, and a new fault detection chart-based GLR is developed. Lastly, an enhanced interval reconstruction approach for fault isolation is developed. The proposed strategy is applied for distillation column process monitoring and air quality monitoring network.
引用
收藏
页数:17
相关论文
共 54 条
[1]  
Ait Izem Tarek, 2015, IFAC - Papers Online, V48, P1402, DOI 10.1016/j.ifacol.2015.09.721
[2]   On the application of interval PCA to process monitoring: A robust strategy for sensor FDI with new efficient control statistics [J].
Ait-Izem, Tarek ;
Harkat, M-Faouzi ;
Djeghaba, Messaoud ;
Kratz, Frederic .
JOURNAL OF PROCESS CONTROL, 2018, 63 :29-46
[3]  
AitIzem T, 2015, P INT C AUT CONTR TE, P101
[4]   Reconstruction-based contribution for process monitoring [J].
Alcala, Carlos F. ;
Qin, S. Joe .
AUTOMATICA, 2009, 45 (07) :1593-1600
[5]  
[Anonymous], 2005, Fault-diagnosis systems: An introduction from fault detection to fault tolerance
[6]  
[Anonymous], 2012, Data-Driven Methods for Fault Detection and Diagnosis in Chemical Processes
[7]   Monitoring of wastewater treatment plants using improved univariate statistical technique [J].
Baklouti, Imen ;
Mansouri, Majdi ;
Ben Hamida, Ahmed ;
Nounou, Hazem ;
Nounou, Mohamed .
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2018, 116 :287-300
[8]  
Bock H.-H., 2012, Analysis of symbolic data: exploratory methods for extracting statistical information from complex data
[9]   Multiscale PLS-based GLRT for fault detection of chemical processes [J].
Botre, Chiranjivi ;
Mansouri, Majdi ;
Karim, M. Nazmul ;
Nounou, Hazem ;
Nounou, Mohamed .
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2017, 46 :143-153
[10]   Kernel PLS-based GLRT method for fault detection of chemical processes [J].
Botre, Chiranjivi ;
Mansouri, Majdi ;
Nounou, Mohamed ;
Nounou, Hazem ;
Karim, M. Nazmul .
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2016, 43 :212-224