Abstract
ABSTRACT Internet of Things (IoT) enabled manufacturing systems can monitor machine health based on real-time data generation, monitoring, control and precise decision-making. In this work, an analytical model-based maintenance scheme is proposed for an IoT-enabled hybrid flow shop (HFS). This model estimates the machine health index by fusing performance metrics data such as cycle time, work in process (WIP) and squared coefficient of variance (SCV) service time. These performance metrics reveal hidden risk factors of the machine’s health state. A particular maintenance activity can be proposed before the failure of the machine based on a threshold value of performance metrics data. The proposed maintenance scheme is optimised using interval type-2 fuzzy logic systems (IT2FLSs). The developed model is also validated with a case study in an IoT-enabled HFS.
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