Analyzing the Influence of Differential Constraints in Possible Conflict and ARR Computation

Diagnosis of real world problems demands the integration of different techniques from several research fields. In Model-based Diagnosis, both Artificial Intelligence and Control Theory communities have provided different but complementary approaches. Recent works, known as BRIDGE proposal, provided a common framework for the integration of techniques for static systems. This work proposes the extension of the BRIDGE framework for a specific class of dynamic systems, thus analyzing the influence of dynamic constraints in the behavior estimation capabilities for two Model-based Diagnosis techniques: Possible Conflicts and Analytical Redundancy Relations obtained through structural analysis. Results show the strong similarities between them, and provide new ways for integration of techniques from both areas. Additionally, algorithms computing Possible Conflicts provide the implicit structural model for state observer design with no extra knowledge added in the model. Results on a case study are provided, then compared and discussed against existing proposals.

[1]  J. Lunze,et al.  Diagnosis of Complex Systems: Bridging the Methodologies of the FDI and DX Communities , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  M. Nyberg,et al.  Minimal Structurally Overdetermined sets for residual generation: A comparison of alternative approaches , 2009 .

[3]  Mattias Nyberg,et al.  A Mixed Causality Approach to Residual Generation Utilizing Equation System Solvers and Differential : Algebraic Equation Theory , 2008 .

[4]  Vicenç Puig,et al.  TOWARDS A BETTER INTEGRATION OF PASSIVE ROBUST INTERVAL-BASED FDI ALGORITHMS , 2006 .

[5]  Marcel Staroswiecki,et al.  Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[7]  Bin Jiang,et al.  Link between high-gain observer-based and parity space residuals for FDI , 2004 .

[8]  Daniele Theseider Dupré,et al.  A Spectrum of Definitions for Temporal Model-Based Diagnosis , 1998, Artificial Intelligence.

[9]  Mattias Krysander,et al.  An Efficient Algorithm for Finding Minimal Overconstrained Subsystems for Model-Based Diagnosis , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[10]  Oskar Dressler.  On-Line Diagnosis and Monitoring of Dynamic Systems based on Qualitative Models and Dependency-recording Diagnosis Engines , 1996, ECAI.

[11]  Carlos Alonso González,et al.  Improving robustness in consistency-based diagnosis using possible conflicts , 2008, ECAI.

[12]  Carlos Alonso González,et al.  Possible conflicts: a compilation technique for consistency-based diagnosis , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2006 .

[14]  Johan de Kleer,et al.  Fundamentals of model-based diagnosis , 2003 .

[15]  Marcel Staroswiecki,et al.  Structural Analysis of Fault Isolability in the DAMADICS benchmark , 2006 .

[16]  Luca Console,et al.  Readings in Model-Based Diagnosis , 1992 .