Efficient Model Based Diagnosis

TL;DR

This paper presents an efficient model-based diagnostic method for systems with causal component relations, achieving quadratic worst-case complexity and linear complexity in low-connectivity cases, applicable to dynamic and looped systems.

Contribution

It introduces a diagnostic process with quadratic worst-case complexity that adapts to system connectivity and extends to dynamic and looped systems.

Findings

Diagnostic process has ${ m O}(n^2)$ worst-case complexity.

In low-connectivity systems, complexity reduces to linear.

Applicable to dynamic systems with options for intermittent fault detection.

Abstract

In this paper an efficient model based diagnostic process is described for systems whose components possess a causal relation between their inputs and their outputs. In this diagnostic process, firstly, a set of focuses on likely broken components is determined. Secondly, for each focus the most informative probing point within the focus can be determined. Both these steps of the diagnostic process have a worst case time complexity of ${\cal O}(n^2)$ where $n$ is the number of components. If the connectivity of the components is low, however, the diagnostic process shows a linear time complexity. It is also shown how the diagnostic process described can be applied in dynamic systems and systems containing loops. When diagnosing dynamic systems it is possible to choose between detecting intermitting faults or to improve the diagnostic precision by assuming non-intermittency.