An Efficient Model-based Diagnosis Engine for Hybrid Systems using Structural Model Decomposition

Abstract

Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, and embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) architecture offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. How- ever, HyDE faces some problems regarding performance in terms of time and space complexity. This paper focuses on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic benchmark problem, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.