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A Model for Predictive Maintenance Based on Asset Administration Shell
| Content Provider | MDPI |
|---|---|
| Author | Cavalieri, Salvatore Salafia, Marco Giuseppe |
| Copyright Year | 2020 |
| Description | Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. 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. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance. |
| Starting Page | 6028 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s20216028 |
| Journal | Sensors |
| Issue Number | 21 |
| Volume Number | 20 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-23 |
| Access Restriction | Open |
| Subject Keyword | Sensors Manufacturing Engineering Predictive Maintenance Industry 4.0 Digital Twin Asset Administration Shell Cps Iiot |
| Content Type | Text |
| Resource Type | Article |
