Predicting Maintenance Means Planning Maintenance
More than 30% of life-cycle costs for rail operators are related to maintenance. Scheduled maintenance provides core backstop but does not always prevent unplanned events. An unscheduled issue can have a large impact on the overall network and customer satisfaction. Predicting these anomalies can increase reliability and at the same time reduce cost.
Freya’s data analytics team uses engineering principals to develop, predictive models using our customers maintenance, usage and sensor data. We have developed highly accurate predictive models that provide maintenance with the knowledge to greatly improve asset availability.