A leading rail vehicle manufacturing and maintenance company needed to accurately estimate the service life of critical components in its fleet. Operating conditions, geographical environment or weather influenced wear patterns, and the lack of a predictive tool made maintenance planning and cost control difficult.
A system based on data analytics and artificial intelligence was developed , capable of calculating the remaining life cycle of each component, identifying key wear factors. In a second phase, we moved towards a detailed analysis system by component, unit, train and fleet, facilitating early detection of deviations and enabling data-driven decision making.
We created a predictive system that accurately estimates the wear of railway components, optimizing maintenance, reducing costs and improving operational planning.
- Identification of key indicators: Environmental, operational and technical factors.
- Life prediction: Models that accurately anticipate wear.
- Multilevel analysis: From the individual component to the entire fleet.
- Operational agility: Drastic reduction of the time required to generate reports and diagnostics.
