Learning diagnosis models using variable-fidelity component model libraries

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Feldman A. (Author) Other; Provan G. (Author) Other; Rui Abreu (Author) FEUP; De Kleer J. (Author) Other

Conference proceedings International

Title: IFAC PAPERSONLINE

Pages: 428-433

15th IFAC Symposium on Information Control Problems in Manufacturing

Ottawa, CANADA, MAY 11-13, 2015

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5