Adaptive remote maintenance of rolling stocks is provided by machine-learning (28) of rules. Existing rules or models are automatically updated. Machine learning (28) is applied to establish a more efficient rule set. Rules may be replaced, generalized, or otherwise adapted based on interaction (26) by the dispatchers with the results of the current rules. The acceptance or discarding of an event by the dispatcher is used as a ground truth for supervised machine learning (28) of a new rule. The machine learning (28) uses user feedback to update the rule set.
