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dc.contributor.authorPourhejazy, Pourya
dc.date.accessioned2021-12-29T10:07:31Z
dc.date.available2021-12-29T10:07:31Z
dc.date.issued2021-10-15
dc.description.abstractSmart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.en_US
dc.identifier.citationPourhejazy. Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework. Sensors. 2021en_US
dc.identifier.cristinIDFRIDAID 1957128
dc.identifier.doi10.3390/s21206860
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10037/23535
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalSensors
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleSmart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Frameworken_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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