The concept of residuals for fault localization in discrete event systems

In this paper an approach for fault localization in closed-loop Discrete Event Systems is proposed. The presented diagnosis method allows fault localization using a fault-free system model to describe the expected system behavior. Via a systematic comparison of the observed and the expected behavior, a fault can be detected and a set of fault candidates is determined. Inspired by residuals known from diagnosis in continuous systems, different set operations are introduced to generate the fault candidate set. After fault detection and a first fault localization, a procedure is given to render the fault localization more precisely by an analysis of the further observed system behavior. Special emphasis is given to the use of identified models for the fault-free system behavior. The approach is explained using a laboratory manufacturing facility.

[1]  Jean-Jacques Lesage,et al.  Black-box identification of discrete event systems with optimal partitioning of concurrent subsystems , 2010, Proceedings of the 2010 American Control Conference.

[2]  G. Collewet,et al.  Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. , 2004, Magnetic resonance imaging.

[3]  Shahin Hashtrudi-Zad,et al.  Fault diagnosis in discrete-event systems: incorporating timing information , 2005, IEEE Transactions on Automatic Control.

[4]  C. Seatzu,et al.  Fault diagnosis and identification of discrete event systems using Petri nets , 2008, 2008 9th International Workshop on Discrete Event Systems.

[5]  Jean-Jacques Lesage,et al.  Identification of Discrete Event Systems - Implementation Issues and Model Completeness , 2010, ICINCO.

[6]  Stéphane Lafortune,et al.  Failure diagnosis using discrete-event models , 1996, IEEE Trans. Control. Syst. Technol..

[7]  David Hearshen,et al.  Correlations between Magnetic Resonance Spectroscopy and Image-guided Histopathology, with Special Attention to Radiation Necrosis , 2002, Neurosurgery.

[8]  Stéphane Klein,et al.  Identification of discrete event systems for fault detection purposes , 2005 .

[9]  Bradford A Moffat,et al.  Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[11]  Vicenç Puig,et al.  Diagnosis of timed automata: Theory and application to the DAMADICS actuator benchmark problem , 2006 .

[12]  Doaa Mahmoud-Ghoneim,et al.  The impact of image dynamic range on texture classification of brain white matter , 2008, BMC Medical Imaging.

[13]  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).

[14]  P. Tofts Quantitative MRI of the Brain , 2003 .

[15]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[16]  M. Viergever,et al.  Neuronavigation and surgery of intracerebral tumours , 2006, Journal of Neurology.

[17]  T. Ihalainen,et al.  MRI quality control: six imagers studied using eleven unified image quality parameters , 2004, European Radiology.

[18]  P. M. Walker,et al.  V. Multi-center trial with protocols and prototype test objects for the assessment of MRI equipment , 1988 .

[19]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[20]  L. Axel,et al.  Quality assurance methods and phantoms for magnetic resonance imaging: report of AAPM nuclear magnetic resonance Task Group No. 1. , 1990, Medical physics.

[21]  Véronique Carré-Ménétrier,et al.  Decentralized diagnosis based on Boolean discrete event models: application on manufacturing systems , 2008 .

[22]  Walter Ukovich,et al.  On-line fault detection in discrete event systems by Petri nets and integer linear programming , 2009, Autom..

[23]  T. Mikkelsen,et al.  Correlation between Magnetic Resonance Spectroscopy Imaging and Image-guided Biopsies: Semiquantitative and Qualitative Histopathological Analyses of Patients with Untreated Glioma , 2001, Neurosurgery.

[24]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[25]  Thomas Moor,et al.  Supervisory control of hybrid systems via l-complete approximations , 1998 .

[26]  Stéphane Lafortune,et al.  Failure diagnosis using discrete event models , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[27]  Lee Friedman,et al.  Report on a multicenter fMRI quality assurance protocol , 2006, Journal of magnetic resonance imaging : JMRI.