Digital twins in condition-based maintenance apps: A case study for train axle bearings

Digital Twins (DTs) are gaining popularity in the context of the fourth industrial revolution to replicate physical equipment and systems in the digital world. DTs promise increased productivity and sustainable performance by integrating data, models, and decision-support systems. However, before realizing the potential benefits of DTs for maintenance management, several challenges need to be addressed, including a lack of conceptual basis, functional description, and established requirements. Hence, the paper presents, in a practical manner, how to cover this gap in digital configurations for maintenance management, designed to benefit of DTs. The scope of the paper includes the design and implementation of an innovative condition-based maintenance application (CBM App) based on a DT of train axle bearings, and uses a generic framework for digital maintenance management for the functional description of the DT within the CBM App. The paper provides details of the models and algorithms used to build the DT and ensures that recommended features are fulfilled. To test the DT's effectiveness and robustness, the design and framework are implemented in real CBM applications of TALGO, a high-speed train manufacturer. These tools are deemed helpful for easing DT implementation within the CBM App and can be replicated in other operational contexts. •Characterizing a comprehensive DT for CBM purposes.•Explanation of modelling solutions for detection, diagnosis, and prognosis.•Explain data ETL to be used in models training and validation.•Describing maintenance department interaction with the Digital Twin.•Ensuring that the developed DT fulfils DT requirements.