AI-Assisted Troubleshooting for Railway Maintenance

This AI solution assists maintenance teams in diagnosing faults in railway workshops by combining image, video, audio, technical documentation, and work order analysis. It enables the capture of evidence, identification of failure modes, guidance through root cause analysis, and the generation of fully traceable maintenance records.

Railway maintenance workshops play a critical role in ensuring the availability, reliability, and safety of rolling stock. However, many inspections and diagnostic processes still rely heavily on expert judgment, fragmented documentation, and manual records, leading to variability between technicians, knowledge loss, and limited traceability of decisions.

This use-case introduces an intelligent diagnostic assistant for railway maintenance. Technicians can capture images, video, audio, or text using a tablet, camera, or mobile device, and the solution analyzes this evidence through multimodal AI models. The system can detect visual anomalies such as wear, insufficient lubrication, corrosion, cracks, deformations, leaks, overheating, or missing components, and correlate them with known failure modes.

The solution guides technicians step by step throughout the inspection process. It requests additional evidence when needed, cross-references information with technical manuals, procedures, historical work orders, and previous diagnoses, and supports the development of a structured root cause analysis. It can also incorporate condition-monitoring data, such as oil analysis, vibration readings, or operational variables, to enrich the diagnostic process. The result is a more consistent, explainable, and traceable diagnosis, supported by documented evidence and actionable recommendations.

In addition, the solution can automatically record relevant information in maintenance systems such as CMMS, EAM, or SAP, reducing administrative workload and improving data quality. The workflow can include expert validation, continuous learning through workshop feedback, and the creation of an auditable digital history. This history can be used to train new technicians, identify recurring patterns, and support future predictive maintenance initiatives.

The solution can initially focus on specific components or failure modes and then scale progressively across additional workshops, fleets, or asset families. It can be deployed in cloud, on-premises, or edge architectures, depending on connectivity, cybersecurity, and operational requirements.

Expected benefits include reduced diagnostic time, fewer errors and rework, standardized technical criteria, preservation of expert knowledge, improved inspection traceability, and a strong foundation for advancing toward predictive maintenance and integrated asset management.

Miguel Ángel Rodríguez López

Subject Matter Expert SDG Group Spain