Abstract
Manufacturing systems are making a shift towards intelligent and predictive Cyber Physical Systems (CPS) with enhanced sensing and communication capabilities. Current maintenance activities are based on predefined schedules, without considering the working state of the equipment and thus leading to over-maintained machinery. The cost related to the production process interruption and for dispatching maintenance personnel to restore the equipment to proper working conditions is high.In this context, this study presents an extendable and re-usable scheduling approach supporting multiple heterogeneous inputs and outputs. Predictive maintenance indicators from the monitored equipment are used for scheduling maintenance operations in line with the existing schedule. A web-service architecture is adopted towards supporting highly different use-cases, such as equipment providers and/or manufacturers. The incorporation of maintenance activities to the production schedule may result in a reduction of maintenance breakdowns and thus increased productivity. The proposed approach has been applied to cases deriving from the automotive and steel production industries.
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