Learning Diagnosis Models Using Variable-Fidelity Component Model Libraries★. Issue 21 (2015)

• Record Type: Journal Article
• Authors: Feldman, Alexander; Provan, Gregory; Abreu, Rui; Kleer, Johan de
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 21(2015)
• Page Start: 428
• Page End: 433
• Publication Date: 2015
• Subjects: Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.09.564
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: System models that are used in model-based diagnosis are often composed of components drawn from component libraries. In these component libraries, there may be multiple systems of equations per component (component implementations). For example, a component may be modeled as a non-linear system (high-fidelity model), linear system, and a qualitative system (low-fidelity model). Choosing the right component model for system diagnosis is a difficult task and requires a search in the space of all possible component type combinations. In this paper we propose a method that automates this task and computes a system model that optimizes a set of diagnostic metrics in a set of diagnostic scenarios. Initial experimental results show that having linear models of some of the components in a system preserves the diagnostic accuracy and isolation time while, at the same time, improves the computational complexity and numerical stability.