MARTIN: An End-to-end Microservice Architecture for Predictive Maintenance in Industry 4.0

Abdessalam Elhabbash, Kamil Rogoda, Yehia Elkhatib

Abstract: The amount of data generated in Industry 4.0 and the introduction of advanced data analytics support establishing “smart factories” and one of its crucial characteristics - predictive maintenance. Current solutions primarily focus on offline predictions and do not provide end-to-end scalable solutions. Furthermore, there is a lack of support for incremental ma-chine learning in predictive maintenance. This paper addresses these limitations by proposing MARTIN, a scalable microservice architecture for predictive maintenance that can collect, store, and analyse data, and make decisions based on the machine state. The architecture uses incremental learning as the basis for predictions. The designed system was implemented and its performance was evaluated experimentally. The results show that the solution can provide high prediction accuracy in terms of practical processing time.