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DOI	10.1016/j.jprocont.2021.10.006
	OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes
	Bhadriraju, Bhavana; Kwon, Joseph Sang-Il; Khan, Faisal
通讯作者	Kwon, JSI (corresponding author), Texas A&M Univ, Artie McFerrin Dept Chem Engn, College Stn, TX 77845 USA.
来源期刊	JOURNAL OF PROCESS CONTROL
ISSN	0959-1524
EISSN	1873-2771
出版年	2021
卷号	107 页码:114-126
英文摘要	With the increasing process complexities, data-driven fault prognosis has emerged as a promising fault management tool that predicts and manages abnormal events well in advance. In this paper, we develop a fault prognosis framework named 'OASIS-P' by integrating operable adaptive sparse identification of systems (OASIS), which is a data-driven adaptive modeling technique, with a risk based process monitoring approach and contribution plots. Firstly, OASIS is employed with the risk assessment procedure for the prediction of impending faults. As the OASIS model is adaptive, it copes with the initial fault symptoms and forecasts the future behavior of the process under faulty conditions reasonably well, thereby providing an early fault prediction. Next, the fault isolation step is immediately initiated using contribution plots to identify the faulty variables. Unlike in fault diagnosis, the problem of ambiguity in interpreting contribution results due to fault propagation is not an issue in fault prognosis, if the fault isolation step is implemented at an early stage of the fault before it affects the other variables. Hence, the contribution plots together with OASIS can proactively monitor the process in real-time. As a case study, we demonstrate OASIS-P for fault prognosis of a reactor-separator system. (C) 2021 Elsevier Ltd. All rights reserved.
英文关键词	Nonlinear systems Sparse model Neural networks Risk assessment Contribution plots Fault prediction Fault isolation
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000715747400006
WOS关键词	DYNAMIC RISK-ASSESSMENT ; REGRESSION ; DECOMPOSITION ; MODELS ; FILTER
WOS类目	Automation & Control Systems ; Engineering, Chemical
WOS研究方向	Automation & Control Systems ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/367815
作者单位	[Bhadriraju, Bhavana; Kwon, Joseph Sang-Il; Khan, Faisal] Texas A&M Univ, Artie McFerrin Dept Chem Engn, College Stn, TX 77845 USA; [Bhadriraju, Bhavana; Kwon, Joseph Sang-Il] Texas A&M Univ, Texas A&M Energy Inst, College Stn, TX 77845 USA; [Bhadriraju, Bhavana; Khan, Faisal] Texas A&M Univ, Mary Kay OConnor Proc Safety Ctr, College Stn, TX 77845 USA
推荐引用方式 GB/T 7714	Bhadriraju, Bhavana,Kwon, Joseph Sang-Il,Khan, Faisal. OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes[J],2021,107:114-126.
APA	Bhadriraju, Bhavana,Kwon, Joseph Sang-Il,&Khan, Faisal.(2021).OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes.JOURNAL OF PROCESS CONTROL,107,114-126.
MLA	Bhadriraju, Bhavana,et al."OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes".JOURNAL OF PROCESS CONTROL 107(2021):114-126.