In recent years, the integration of Digital Twins (DT) for the adoption of smarter maintenance strategies has grown exponentially in different industrial sectors. New IoT and edge computing systems are being developed for this purpose, however, there are still some open issues and challenges to be solved. Firstly, this paper presents new approaches to the initial dependencies of the studied solution and make a new proposal to improve the interoperability of the presented system. Secondly, this paper provides a methodology applicable to similar developments of edge-based AI (Artificial Intelligence) solution, which comprises of four phases: the presentation of the multi-objective problem and the pre-selection of AI-based models, the description of the evaluation architecture, the profiling of the different models for the selection of the most suitable one and explainable AI strategies for getting insights of the selected model. Finally, it presents a use case of an edge-solution for the railway catenary geometry diagnostic (stagger amplitude of the overhead wire), saving the interoperability of the message exchange with other systems is provided.