LPP’s Train Diagnostics system uses AI-driven predictive maintenance to improve operational reliability and reduce maintenance costs. By continuously monitoring key components, it detects early signs of failure, enabling timely servicing and reducing unexpected downtime.

The system consists of three subsystems: a diagnostic recorder unit, a train-borne recorder, and a graphical user interface for data display and analysis. These components work together to capture, process, and visualise diagnostic data across the train.

Machine learning algorithms are applied to this data to identify patterns and correlations based on historical performance and expert knowledge. This enables early detection of faults and supports predictive decision-making.

By forecasting equipment condition and issuing early maintenance alerts, the system extends asset lifespans and reduces service disruptions. It also supports efficient logistics planning across the rail supply chain. The system can also capture audio between train operators and control centres, linking recordings to time and GPS data for post-incident analysis.