Predictive Maintenance (PdM) techniques are designed to assist in determining the condition of in-service equipment, enabling the estimation of when maintenance should be performed. Subsequently, these techniques aim to optimise the performance and lifespan of equipment by continuously assessing its system health. The PdM platform will empower maintenance teams to monitor assets and identify conditions that may indicate potential failures, ultimately allowing maintenance teams to prevent such occurrences. The PdM system solution incorporates physics and data models specific to maintenance processes. The system will develop machine learning models to assess equipment via online data, such as sensor installations, and offline data.

PdM aims to minimise maintenance and repair costs, reduce unplanned downtime, enhance train efficiency and availability, prioritise safety, and extend asset lifespans. These improvements will directly enhance the Mean Time Between Failures (MTBF) of the train and enhance customer satisfaction.

Based on the frequency of failures, three critical components have been identified: Brake, Bogie, and Wheel Rail Interface (RI) as the initial phase of PdM implementation.

Utilising AVEVA’s PI Systems as the platform for PdM, it will serve as a monitoring and decision-making tool to determine the criticality of assets. With the addition of AVEVA Mobile Operator, maintenance work inspection is enhanced by providing a digital platform for maintenance teams.