Hybrid Model Learning for System Health Monitoring

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. Copyright (C) 2022 The Authors.