Practical Model-Based Diagnosis with Qualitative Possibilistic Uncertainty

TL;DR

This paper introduces a fault diagnosis method using incomplete models and possibilistic uncertainty, enabling efficient and exception-tolerant identification of faults in complex systems.

Contribution

It presents a novel approach combining qualitative possibilistic uncertainty with component-based models for fault diagnosis, improving focus and robustness.

Findings

Effective fault isolation with incomplete models

Ability to distinguish varying degrees of certainty in effects

Successful application demonstrated on a realistic example

Abstract

An approach to fault isolation that exploits vastly incomplete models is presented. It relies on separate descriptions of each component behavior, together with the links between them, which enables focusing of the reasoning to the relevant part of the system. As normal observations do not need explanation, the behavior of the components is limited to anomaly propagation. Diagnostic solutions are disorders (fault modes or abnormal signatures) that are consistent with the observations, as well as abductive explanations. An ordinal representation of uncertainty based on possibility theory provides a simple exception-tolerant description of the component behaviors. We can for instance distinguish between effects that are more or less certainly present (or absent) and effects that are more or less certainly present (or absent) when a given anomaly is present. A realistic example illustrates the benefits of this approach.