An Incremental Hybrid System Diagnoser Automaton Enhanced by Discernibility Properties

Abstract

This paper proposes a method to track the system mode and diagnose a hybrid system without building an entire diagnoser off-line. The method is supported by a hybrid automaton (HA) model that represents the hybrid system continuous and discrete behavioral dynamics. This model is built on request through parallel composition of the component HA models. Diagnosis is performed by interpreting the events and measurements issued by the physical system directly on the HA model. This interpretation allows us to construct the useful parts of the diagnoser developing only the branches that are required to explain the occurrence of incoming events. The resulting diagnoser adapts to the system operational life and is much less demanding in terms of memory storage than the entire diagnoser. In addition to this feature, the proposed framework subsumes previous works in that it copes with both structural and nonstructural faults. The method is validated by the application to a case study based on the sewer network of the city of Barcelona.