AID4TRAIN: Artificial Intelligence-Based Diagnostics for TRAins and INdustry 4.0

Diagnostic data logs generated by systems components represent the main source of information about the system run-time behavior. However, as faults typically lead to multiple reported errors that propagate to other components, the analysts' work is hardened by digging in cascading diagnostic messages. Root cause analysis can help to pinpoint faults from the failures occurred during system operation but it is unpractical for complex systems, especially in the context of Industry 4.0 and Railway domains, where smart control devices continuously generate high amount of logs. The AID4TRAIN project aims to improve root cause analysis in both Industry 4.0 and Railway domains leveraging AI techniques to automatically infer a fault model of the target system from historical diagnostic data, which can be integrated with the system experts knowledge. The resulting model is then leveraged to create log filtering rules to be applied on previously unseen diagnostic data to identify the root cause of the occurred problem. This paper introduces the AID4TRAIN framework and its implementation at the current project stage. Further, a preliminary case study in the railway domain is presented.