Journal
JOURNAL OF PROCESS CONTROL
Volume 98, Issue -, Pages 66-78Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2020.12.007
Keywords
Fault isolation; Structured sparsity; Multivariate statistical analysis; ADMM
Funding
- National Natural Science Foundation of China [61673358, 61973145]
- NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization, China [U1709215]
Ask authors/readers for more resources
This article introduces a fault isolation framework based on structured sparsity modeling, which incorporates process structure information into fault diagnosis methods to achieve more accurate fault isolation.
In order to improve the fault diagnosis capability of multivariate statistical methods, this article introduces a fault isolation framework based on structured sparsity modeling. The developed method relies on the reconstruction based contribution analysis and the process structure information can be incorporated into the reconstruction objective function in the form of structured sparsity regularization terms. The structured sparsity terms allow selection of fault variables over structures like blocks or networks of process variables, hence more accurate fault isolation can be achieved. Four structured sparsity terms corresponding to different kinds of process information are considered, namely, partially known sparse support, block sparsity, clustered sparsity and tree-structured sparsity. The optimization problems involving the structured sparsity terms can be solved using the Alternating Direction Method of Multipliers (ADMM) algorithm, which is fast and efficient. Through a simulation example and an application study to a coal-fired power plant, it is verified that the proposed method can better isolate faulty variables by incorporating process structure information. (c) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available