Health monitoring approaches are usually either model-based or data-based. This article aims at using available data to learn a hybrid model to profit from both the data-based and model-based advantages. The hybrid model is represented under the Heterogeneous Petri Net formalism. The learning method is composed of two steps: the learning of the Discrete Event System (DES) structure using a clustering algorithm (DyClee) and the learning of the continuous system dynamics using two regression algorithms (Support Vector Regression or Random Forest Regression). The method is illustrated with an academic example.