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HyMU: a software for Hybrid Systems Health Monitoring under Uncertainty

Elodie Chanthery 1 Pauline Ribot 1 Amaury Vignolles 1
1 LAAS-DISCO - Équipe DIagnostic, Supervision et COnduite
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : HyMU (Hybrid system Monitoring under Uncertainty) is a software developed in Python by the DISCO team at LAAS-CNRS. It was designed to simulate, diagnose and prognose hybrid systems using model-based methods. Its main feature is to deal with many types of uncertainties (modeling uncertainty, unreliable observations and unknown future inputs). A multimode representation of the hybrid system has to be established by specifying continuous and discrete evolutions defining its behavior and its degradation. From this representation , HyMU computes a Hybrid Particle Petri Net (HPPN) model, a diagnoser and a prognoser. The HPPN model can be simulated with an input scenario to compute the system outputs and health mode. From the HPPN model and a set of observations , the diagnoser and the prognoser compute the current and future mode beliefs, the mode tra-jectories and the predicted Remaining Useful Life (RUL). This paper describes HyMU, its function-ality and gives some examples of results.
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Elodie Chanthery, Pauline Ribot, Amaury Vignolles. HyMU: a software for Hybrid Systems Health Monitoring under Uncertainty. 30th International Workshop on Principles of Diagnosis DX'19, Nov 2019, Klagenfurt, Austria. ⟨hal-02886575⟩

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