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dc.contributor.authorYang, Wei-Ting
dc.contributor.authorReis, Marco
dc.contributor.authorBorodin, Valeria
dc.contributor.authorJuge, Michel
dc.contributor.authorRoussy, Agnès
dc.date.accessioned2023-08-22T12:04:43Z
dc.date.available2023-08-22T12:04:43Z
dc.date.created2022-09-06T14:05:51Z
dc.date.issued2022
dc.identifier.citationControl Engineering Practice. 2022, 127 .en_US
dc.identifier.issn0967-0661
dc.identifier.urihttps://hdl.handle.net/11250/3085257
dc.description.abstractProcess monitoring is a critical activity in manufacturing industries. A wide variety of data-driven approaches have been developed and employed for fault detection and fault diagnosis. Analyzing the existing process monitoring schemes, prediction accuracy of the process status is usually the primary focus while the explanation (diagnosis) of a detected fault is relegated to a secondary role. In this paper, an interpretable unsupervised machine learning model based on Bayesian Networks (BN) is proposed to be the fundamental model supporting the process monitoring scheme. The proposed methodology is aligned with the recent efforts of eXplanatory Artificial Intelligence (XAI) for knowledge induction and decision making, now brought to the scope of advanced process monitoring. A BN is capable of combining data-driven induction with existing domain knowledge about the process and to display the underlying causal interactions of a process system in an easily interpretable graphical form. The proposed fault detection scheme consists of two levels of monitoring. In the first level, a global index is computed and monitored to detect any deviation from normal operation conditions. In the second level, two local indices are proposed to examine the fine structure of the fault, once it is signaled at the first level. These local indices support the diagnosis of the fault, and are based on the individual unconditional and conditional distributions of the monitored variables. A new labeling procedure is also proposed to narrow down the search and identify the fault type. Unlike many existing diagnosis methods that require access to faulty data (supervised diagnosis methods), the proposed diagnosis methodology belongs to the class that only requires data under normal conditions (unsupervised diagnosis methods). The effectiveness of the proposed monitoring scheme is demonstrated and validated through simulated datasets and an industrial dataset from semiconductor manufacturing.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectBayesian Network (BN)en_US
dc.subjecteXplanatory Artificial Intelligenceen_US
dc.subjectFault Detectionen_US
dc.subjectInterpretable Machine Learningen_US
dc.titleAn interpretable unsupervised Bayesian network model for fault detection and diagnosisen_US
dc.title.alternativeAn interpretable unsupervised Bayesian network model for fault detection and diagnosisen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holderElsevieren_US
dc.source.pagenumber17en_US
dc.source.volume127en_US
dc.source.journalControl Engineering Practiceen_US
dc.identifier.doi10.1016/j.conengprac.2022.105304
dc.identifier.cristin2049210
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal