Abstract: A method for asset health management is provided. The method includes receiving, first maintenance data, first booking data, first vehicle data, first operational data, and a labelled dataset for a set of vehicles over a first time-interval. The labelled dataset includes at least an actual health index for each vehicle observed over the first time-interval. The method includes determining a first plurality of features and a corresponding first plurality of feature values based on the first maintenance data, the first booking data, the first vehicle data, and the first operational data. The method includes training a prediction model based on the first plurality of features, the first plurality of feature values, and the labelled dataset. The method includes determining a health index of a target vehicle based on the trained first prediction model and a first dataset associated with the target vehicle.

Abstract: A method for predictive maintenance of a component of a vehicle includes collection of first operational data, from a telematics device of the vehicle, for a set of parameters associated with the component. From the first operation data, a first dataset is selected based on an engine status of an engine of the vehicle. The first dataset is processed to obtain a plurality of feature values and the plurality of feature values are segregated into a plurality of clusters. A classifier is trained based on the plurality of clusters to determine the health status of the component. Real-time or near real-time operational data for the set of parameters is received, from the telematics device of the vehicle. The received operational data is used as an input to the trained classifier and the health status of the component is determined based on an output of the trained classifier.