1. A method of prognosing a mechanical system comprising: receiving measurement data corresponding to the mechanical system; extracting one or more features from the received measurement data by decomposing the measurement data into a feature space; selecting a prediction model from a plurality of prediction models for one or more features based at least on part on a degradation status of the mechanical system and a reinforcement learning model, wherein: the reinforcement learning model is defined by a plurality of states, each state corresponding to a particular degradation status; the reinforcement learning model comprises a Q-value for each prediction model at each state and the selected prediction model is the prediction model having largest Q-value at a particular state; generating a predicted feature space by applying the selective prediction model to the feature space; generating a confidence value by comparing the predicted feature space with a normal baseline distribution, a faulty baseline distribution, or a combination thereof; and providing a status of mechanical system based at least in part on the confidence value.