Predictive Maintenance Model based on Asset Administration Shell

Maintenance is one of the most important aspects in industrial and production environment. The availability of huge amount of data coming from sensors and embedded systems, enabled the realisation of Predictive maintenance (PdM). It is an approach that aim to schedule maintenance tasks on the basis of historical data before the occurrence of failures, avoiding machine block downs and reducing the costs due to unnecessary maintenance actions. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. The paper presents a PdM model leveraging on the Asset Administration Shell (AAS) introduced in Reference Architecture Model for Industrie 4.0 (RAMI 4.0) as a means to enhance interoperability and enabling flexibility and re-configuration of the production against a PdM solution.