Monitoring and diagnosing complex, real-world, industrial hybrid systems require accurate and up-to-date models that can adapt to evolving system behaviors. Such systems, characterized by both continuous and discrete dynamics, are best represented by hybrid models. In this article, we present the segmentation step of HyMED (Hybrid Model Enrichment for Diagnosis), a model-based health monitoring and diagnosis method that monitors hybrid systems and automatically updates the system model if necessary. HyMED uses noisy multivariate time series data to dynamically update models, addressing unanticipated degradations and faults. A key feature of HyMED is its online and passive segmentation step (ODS), which enables robust detection of system mode changes in complex, nonlinear time series. Unlike traditional segmentation methods, ODS dynamically determines its segmentation hyperparameters through an automatic parameter selection process. ODS guarantees adaptability without the need for manual adjustment. The effectiveness of HyPED’s segmentation method is demonstrated through a case study on an engine timing system, where its performances are compared to the offline method depicted in the Ruptures library.
Hatte et al. (Sun,) studied this question.