DXPCS: a toolbox for model-based diagnosis of dynamic systems using possible conflicts

Consistency-based diagnosis is a model-based diagnosis approach for the artificial intelligence community which relies upon models of correct behaviour and allows automatic multiple fault detection and isolation. The theory for static systems diagnosis is well established but there is a lack of free available tools implementing these ideas for dynamic systems, making rather difficult its dissemination. In this work we introduce DxPCs, a software tool capable of performing consistency-based diagnosis of continuous dynamic systems whose models can be represented as a set of algebraic differential equations. The diagnosis approach relies upon the possible conflict concept. DxPCs is able to automatically build the simulation models for each PC. Single-fault and multiple-fault scenarios, for both parametric and additive faults, can be injected, and studied. DxPCs allows the integration of different algorithms for fault detection, residual generation and evaluation, together with an incremental version of the minimal-hitting set algorithm for fault localization. The software architecture, together with performance results for one case study, are provided in this paper.