文章基本信息

标题：Learning Diagnosis Models Using Variable-Fidelity Component Model Libraries ★ ★ Supported by SFI grant 12/RC/2289.
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作者：Alexander Feldman ; Gregory Provan ; Rui Abreu 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：21
页码：428-433
DOI：10.1016/j.ifacol.2015.09.564
语种：English
出版社：Elsevier
摘要：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.