Learning diagnosis models using variable-fidelity component model libraries

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Feldman A. (Autor) Outra; Provan G. (Autor) Outra; Rui Abreu (Autor) FEUP; De Kleer J. (Autor) Outra

Ata de Conferência Internacional

Título: IFAC PAPERSONLINE

Páginas: 428-433

15th IFAC Symposium on Information Control Problems in Manufacturing

Ottawa, CANADA, MAY 11-13, 2015

Indexação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00V-39H

DOI: 10.1016/j.ifacol.2015.09.564

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5