From Theory to Practice: Model-Based Diagnosis in Industrial Applications

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Roxane Koitz Franz Wotawa

Abstract

Due to the increasing complexity of technical systems, accurate fault identification is crucial in order to reduce maintenance costs and system downtime. Model-based diagnosis has been proposed as an approach to improve fault localization. By utilizing a system model, possible causes, i.e. defects, for observable anomalies can be computed. Even though model-based diagnosis rests on solid theoretical background, it has not been widely adopted in practice. The reasons are twofold: on the one hand it requires an initial modeling effort and on the other hand a high computational complexity is associated with the diagnosis task in general. In this paper we address these issues by proposing a process for abductive model-based diagnosis in an industrial setting. Suitable models are created automatically from failure assessments available. Further, the compiled system descriptions reside within a tractable space of abductive diagnosis. In or- der to convey the feasibility of the approach we present results of an empirical evaluation based on several failure assessments.

How to Cite

Koitz, . R. ., & Wotawa, F. . (2015). From Theory to Practice: Model-Based Diagnosis in Industrial Applications. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2565
Abstract 3 | PDF Downloads 2

##plugins.themes.bootstrap3.article.details##

Keywords

Model-based diagnosis, model based reasoning, Failure Modes and Effect Analysis (FMEA), Fault identification

Section
Technical Papers