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A two-step procedure for fault detection in the Tennessee Eastman Process simulator
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
2016 (English)Conference paper, Presentation (Refereed)
Abstract [en]

High-technological and complex production processes and high availability and sample frequencies of data in large scale industrial processes need the concurrent development of appropriate statistical control tools and monitoring techniques. Therefore, multivariate control charts based on latent variables are essential tools to detect and isolate process faults.Several Statistical Process Control (SPC) charts have been developed for multivariate and megavariate data, such as the Hotelling T2, MCUSUM and MEWMA control charts as well as charts based on principal component analysis (PCA) and dynamic PCA (DPCA). The ability of SPC procedures based on PCA (Kourti, MacGregor 1995) or DPCA (Ku et al. 1995) to detect and isolate process disturbances for a large number of highly correlated (and time-dependent in the case of DPCA) variables has been demonstrated in the literature. However, we argue that the fault isolation capability and the fault detection rate for processes can be improved further for processes operating under feedback control loops (in closed loop).The purpose of this presentation is to illustrate a two-step method where [1] the variables are pre-classified prior to the analysis and [2] the monitoring scheme based on latent variables is implemented. Step 1 involves a structured qualitative classification of the variables to guide the choice of which variables to monitor in Step 2. We argue that the proposed method will be useful for many practitioners of SPC based on latent variables techniques in processes operating in closed loop. It will allow clearer fault isolation and detection and an easier implementation of corrective actions. A case study based on the data available from the Tennessee Eastman Process simulator under feedback control loops (Matlab) will be presented. The results from the proposed method are compared with currently available methods through simulations in R statistics software.

Place, publisher, year, edition, pages
2016.
Research subject
Quality Technology and Management; Intelligent industrial processes (AERI); Effective innovation and organisation (AERI); Enabling ICT (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-37881Local ID: c0cb4fc8-2b6b-4c2b-9ce1-c879f319d949OAI: oai:DiVA.org:ltu-37881DiVA: diva2:1011379
Conference
Annual Conference of the European Network for Business and Industrial Statistics : 11/09/2016 - 15/09/2016
Projects
Statistiska metoder för förbättring av kontinuerliga tillverkningsprocesser
Note
Godkänd; 2016; 20160701 (bjarne)Available from: 2016-10-03 Created: 2016-10-03Bibliographically approved

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Capaci, FrancescaKulahci, MuratVanhatalo, ErikBergquist, Bjarne
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ReferencesLink to record
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