A Model for Predictive Maintenance Based on Asset Administration Shell

Abstract

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.

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

“…We discussed the results and interpreted them in the perspective of previous studies and research. Predictive maintenances in Cavalieri-Salafia’s model [ 153 ] includes data acquisition from sensors, data manipulation (filtering, transforming, removing noise), aggregation, prediction, decision-making, scheduling, and further monitoring of status and configuration. Similarly, it describes the process of data acquisition, data processing, and machine decision-making [ 154 ].…”

SQL and NoSQL Databases in the Context of Industry 4.0

“…Such flexibility enables the storage of semi-structured data, which best characterizes AAS. Technical reports from the Plattform Industrie 4.0 [34] and academic papers [68][69][70] present AAS implementations in XML and JSON format, which suggests that document-oriented NoSQL systems are advantageous, although it is not the only one capable of storing semi-structured data. This document encoding type is supported by essential communication technologies relevant to the I4.0, such as OPC UA [71] and HTTP.…”

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

“…Existing literature mainly focuses on the development of architectures for addressing lower level interoperability challenges of Industry 4.0, such as distributed storage, data aggregation, and service orchestration (Pisching et al, 2018 ; Bicocchi et al, 2019 ; Fraile et al, 2019 ) as well as big data infrastructures (Pedone and Mezgár, 2018 ; Calabrese et al, 2020 ). A considerable amount of research has also focused on architectures for CPS, digital twins, and AAS (Lee et al, 2015 ; Bader and Maleshkova, 2019 ; Bousdekis et al, 2020a ; Cavalieri and Salafia, 2020 ). For more details, the reader may refer to Moghaddam et al ( 2018 ), Cheng et al ( 2018 ), Fraile et al ( 2019 ), and Zeid et al ( 2019 ).…”