Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/6445
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dc.contributor.authorAbdo, Alien_US
dc.contributor.authorSiam, Jamalen_US
dc.contributor.authorSalah, Bashiren_US
dc.contributor.authorKrid, Mohammeden_US
dc.date.accessioned2020-09-03T10:32:51Z-
dc.date.available2020-09-03T10:32:51Z-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/20.500.11889/6445-
dc.description.abstractProduction-line sensors are essential for synchronisation and execution of the correct workflow in a manufacturing process. Usually, sensor faults lead to serious delays, or even the complete termination of the manufacturing process, to carry out maintenance. Thus, sensor faults compromise the reliability of the manufacturing system and disturb the production schedule. This study proposes a multiple-sensor fault detection and isolation scheme for manufacturing series production lines. The proposed scheme adopts a globalredundancy method, using a digital camera and an ad-hoc video processing algorithm to detect and isolate faulty sensors. The main objective of this research is to preserve continuity of the production workflow and solve the problem of production delays and interruptions. Moreover, the scheme provides the possibility of online and post-process system maintenance. Further, the collected information on sensor false alarm rates can be used for a reliability analysis of the production line. The proposed scheme was tested using a laboratory production line model. The results show that the proposed scheme achieves the established objectives and improves the reliability of the manufacturing processen_US
dc.subjectFault location (Engineering) - Identificationen_US
dc.subjectFault location (Engineering) - Isolationen_US
dc.subjectImage processingen_US
dc.subjectFault-tolerant computingen_US
dc.subjectProgrammable logic devicesen_US
dc.subjectFinite state machineen_US
dc.titleMultiple sensor fault detection and isolation using video processing in production linesen_US
dc.typeArticleen_US
newfileds.departmentArtsen_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectnoneen_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
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