Bridging control and artificial intelligence theories for diagnosis: A survey

Diagnosis is the process of identifying or determining the nature and root cause of a failure, problem, or disease from the symptoms resulting from selected measurements, checks or tests. The different facets of this problem and the wide spectrum of classes of systems make it interesting to several communities and require bridging several theories. Diagnosis is actually a functional fragment in fault management architectures and it must smoothly interact with other functions. This paper presents diagnosis as it is understood in the Control and Artificial Intelligence fields, and exemplifies how different theories of these fields can be synergistically integrated to provide better diagnostic solutions and to achieve improved fault management in different environments.

[1]  S. Gentil,et al.  Combining FDI and AI approaches within causal-model-based diagnosis , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Brian C. Williams,et al.  Hybrid estimation of complex systems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Aditi Chattopadhyay,et al.  Adaptive Residual Useful Life Estimation of a Structural Hotspot , 2010 .

[4]  M. V. Iordache,et al.  Diagnosis and Fault-Tolerant Control , 2007, IEEE Transactions on Automatic Control.

[5]  O. O. Oyeleye,et al.  Qualitative simulation of chemical process systems: Steady‐state analysis , 1988 .

[6]  Audine Subias,et al.  Classification Tool Based on Interactivity Between Expertise and Self-Learning Techniques 1 , 2003 .

[7]  Fuchun Liu,et al.  Safe Diagnosability of Stochastic Discrete Event Systems , 2008, IEEE Transactions on Automatic Control.

[8]  Johan de Kleer,et al.  Readings in qualitative reasoning about physical systems , 1990 .

[9]  Sebastian Thrun,et al.  Real-time fault diagnosis [robot fault diagnosis] , 2004, IEEE Robotics & Automation Magazine.

[10]  Brian C. Williams,et al.  Mode Estimation of Probabilistic Hybrid Systems , 2002, HSCC.

[11]  Sankalita Saha,et al.  Metrics for Offline Evaluation of Prognostic Performance , 2021, International Journal of Prognostics and Health Management.

[12]  Pieter J. Mosterman,et al.  Diagnosis of continuous valued systems in transient operating regions , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[13]  Carine Jauberthie,et al.  Optimal Input Design for a Nonlinear Dynamical Uncertain Aerospace System , 2013, NOLCOS.

[14]  Matthew Daigle,et al.  An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis , 2012 .

[15]  Antonio Vicino,et al.  Optimal estimation theory for dynamic systems with set membership uncertainty: An overview , 1991, Autom..

[16]  Michèle Basseville,et al.  Detection of Changes in Signals and Systems , 1987 .

[17]  A. Rosenfeld,et al.  IEEE TRANSACTIONS ON SYSTEMS , MAN , AND CYBERNETICS , 2022 .

[18]  E. Chanthery,et al.  An AO *-like algorithm implementation for active diagnosis , 2010 .

[19]  Gautam Biswas,et al.  Improving Diagnosability of Hybrid Systems through Active Diagnosis , 2009 .

[20]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[21]  N. de Freitas Rao-Blackwellised particle filtering for fault diagnosis , 2002, Proceedings, IEEE Aerospace Conference.

[22]  P. Frank On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey , 1994 .

[23]  Gautam Biswas,et al.  An Approach to Model-Based Diagnosis of Hybrid Systems , 2002, HSCC.

[24]  Audine Subias,et al.  A Discrete Event Model for Situation Awareness Purposes , 2007 .

[25]  Raymond Reiter,et al.  Characterizing Diagnoses and Systems , 1992, Artif. Intell..

[26]  Paolo Traverso,et al.  Automated Planning: Theory & Practice , 2004 .

[27]  Teresa Escobet,et al.  The Ca~En Diagnosis System and its Automatic Modelling Method , 2001, Computación y Sistemas.

[28]  Nicola Muscettola,et al.  Model-based executive control through reactive planning for autonomous rovers , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[29]  Audine Subias,et al.  Towards Active Diagnosis of Hybrid Systems leveraging Multimodel Identification and a Markov Decision Process , 2015 .

[30]  Philippe Dague,et al.  State Tracking of Uncertain Hybrid Concurrent Systems , 2002 .

[31]  Y. Bar-Shalom,et al.  Multiple-model estimation with variable structure , 1996, IEEE Trans. Autom. Control..

[32]  George Stavrakakis,et al.  Fault detection using parameter estimation , 1989 .

[33]  Richard Washington,et al.  On-board real-time state and fault identification for rovers , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[34]  Eric Walter,et al.  Guaranteed estimation of the parameters of nonlinear continuous‐time models: Contributions of interval analysis , 2011 .

[35]  Mark A. Kramer,et al.  A rule‐based approach to fault diagnosis using the signed directed graph , 1987 .

[36]  Rong Zhou,et al.  Pervasive Diagnosis: The Integration of Diagnostic Goals into Production Plans , 2008, AAAI.

[37]  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.

[38]  J. Ragot,et al.  Fault detection with model parameter structured uncertainties , 1999, 1999 European Control Conference (ECC).

[39]  Patrick Taillibert,et al.  Polynomial Temporal Band Sequences for Analog Diagnosis , 1997, IJCAI.

[40]  Marie-Véronique Le Lann,et al.  From Chemical Process Diagnosis to Cancer Prognosis: An Integrated Approach for Diagnosis and Sensor/Marker Selection , 2011 .

[41]  Louise Travé-Massuyès,et al.  Mode set focused hybrid estimation , 2013, Int. J. Appl. Math. Comput. Sci..

[42]  Pietro Torasso,et al.  Agent Cooperation for Monitoring and Diagnosing a MAP , 2009, MATES.

[43]  Rachid Alami,et al.  An Architecture for Autonomy , 1998, Int. J. Robotics Res..

[44]  Raman K. Mehra,et al.  Optimal input signals for parameter estimation in dynamic systems--Survey and new results , 1974 .

[45]  Jana Kosecka,et al.  Control of Discrete Event Systems , 1992 .

[46]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[47]  K. Ito,et al.  On State Estimation in Switching Environments , 1970 .

[48]  K. Glover,et al.  Identifiability of linear and nonlinear dynamical systems , 1976 .

[49]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[50]  Michèle Basseville,et al.  Detecting changes in signals and systems - A survey , 1988, Autom..

[51]  Takashi Washio,et al.  Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains , 1999, IJCAI.

[52]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[53]  Gautam Biswas,et al.  Hybrid Systems Diagnosis , 2000, HSCC.

[54]  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).

[55]  Pietro Torasso,et al.  Plan Diagnosis and Agent Diagnosis in Multi-agent Systems , 2007, AI*IA.

[56]  Nedialko S. Nedialkov,et al.  Computing reachable sets for uncertain nonlinear hybrid systems using interval constraint propagation techniques , 2009, ADHS.

[57]  Stefan Edelkamp,et al.  Automated Planning: Theory and Practice , 2007, Künstliche Intell..

[58]  Louise Travé-Massuyès,et al.  Hybrid Estimation through Synergic Mode-Set Focusing , 2009 .

[59]  Nico Roos,et al.  Diagnosis of Multi-agent Plan Execution , 2006, MATES.

[60]  Michael W. Hofbaur,et al.  Hybrid Diagnosis with Unknown Behavioral Modes , 2002 .

[61]  Yannick Pencolé,et al.  Diagnosis and prognosis for the maintenance of complex systems , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[62]  Gautam Biswas,et al.  Bayesian Fault Detection and Diagnosis in Dynamic Systems , 2000, AAAI/IAAI.

[63]  Stéphane Lafortune,et al.  Safe diagnosability for fault-tolerant supervision of discrete-event systems , 2005, Autom..

[64]  Xavier Olive,et al.  Active Diagnosis of Hybrid Systems Guided by Diagnosability Properties , 2009 .

[65]  Pietro Torasso,et al.  Intelligent Supervision for Robust Plan Execution , 2011, AI*IA.

[66]  P. Pandurang Nayak,et al.  A Model-Based Approach to Reactive Self-Configuring Systems , 1996, AAAI/IAAI, Vol. 2.

[67]  Brian C. Williams,et al.  Model-based programming of intelligent embedded systems and robotic space explorers , 2003, Proc. IEEE.

[68]  Nacim Ramdani,et al.  A fast method for solving guard set intersection in nonlinear hybrid reachability , 2013, 52nd IEEE Conference on Decision and Control.

[69]  Brian C. Williams,et al.  Diagnosis as Semiring-Based Constraint Optimization , 2004, ECAI.

[70]  X. Olive,et al.  Hybrid Systems Diagnosis by coupling Continuous and Discrete event Techniques , 2008 .

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

[72]  Jamal Daafouz,et al.  Systèmes dynamiques hybrides , 2007 .

[73]  Ramon Sarrate,et al.  Fault detection and isolation of hybrid systems using diagnosers that reason on components , 2012 .

[74]  Yannick Pencolé,et al.  Prognostics for the Maintenance of Distributed Systems , 2008 .

[75]  Ali Alawi,et al.  Real Time Fault Diagnosis , 1997 .

[76]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[77]  Stéphane Lafortune,et al.  Active diagnosis of discrete event systems , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[78]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[79]  Nico Roos,et al.  Diagnosis of single and multi-agent plans , 2005, AAMAS '05.

[80]  Alexander Artikis,et al.  Logic-based event recognition , 2012, The Knowledge Engineering Review.

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

[82]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[83]  Igor V. Nikiforov,et al.  Non-Bayesian Detection and Detectability of Anomalies From a Few Noisy Tomographic Projections , 2007, IEEE Transactions on Signal Processing.

[84]  Jorge Mujica,et al.  Existence of holomorphic mappings with prescribed asymptotic expansions at a given set of points in infinite dimensions , 1981 .

[85]  Sriram Narasimhan,et al.  Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems , 2004 .

[86]  Alain Mille,et al.  A complete chronicle discovery approach: application to activity analysis , 2012, Expert Syst. J. Knowl. Eng..

[87]  Madan M. Gupta,et al.  Approximate reasoning in decision analysis , 1982 .

[88]  Thomas A. Henzinger,et al.  The theory of hybrid automata , 1996, Proceedings 11th Annual IEEE Symposium on Logic in Computer Science.

[89]  Kai Goebel,et al.  Model-Based Prognostics With Concurrent Damage Progression Processes , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[90]  K. Drira,et al.  Self-adapting Strategies guided by Diagnosis and Situation Assessment in Collaborative Communicating Systems , 2010 .

[91]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[92]  Karvel K. Thornber,et al.  Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks , 1998, IEEE Trans. Fuzzy Syst..

[93]  Sylviane Gentil,et al.  Model-based causal reasoning for process supervision , 1994, Autom..

[94]  Raja Sengupta,et al.  Diagnosability of discrete-event systems , 1995, IEEE Trans. Autom. Control..

[95]  Louise Travé-Massuyès,et al.  Set-Theoretic Estimation of Hybrid System Configurations , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[96]  Louise Trave-Massuyes,et al.  Timed fault diagnosis , 2007, 2007 European Control Conference (ECC).

[97]  Christophe Dousson,et al.  Discovering Chronicles with Numerical Time Constraints from Alarm Logs for Monitoring Dynamic Systems , 1999, IJCAI.

[98]  P. Pandurang Nayak,et al.  Back to the Future for Consistency-Based Trajectory Tracking , 2000, AAAI/IAAI.

[99]  Philippe Dague,et al.  Raisonnement causal en physique qualitative , 2004 .

[100]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

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

[102]  Joseph Aguilar-Martin,et al.  Process situation assessment: From a fuzzy partition to a finite state machine , 2006, Eng. Appl. Artif. Intell..

[103]  B. Dubuisson,et al.  Advanced pattern recognition techniques for system monitoring and diagnosis : A survey , 1997 .

[104]  L. Mevel,et al.  Statistical model-based damage detection and localization: subspace-based residuals and damage-to-noise sensitivity ratios , 2004 .

[105]  Louise Travé-Massuyès,et al.  The Consistency Approach to the On-Line Prediction of Hybrid System Configurations1 , 2003, ADHS.

[106]  M. Staroswiecki,et al.  ANALYTICAL REDUNDANCY IN NON LINEAR INTERCONNECTED SYSTEMS BY MEANS OF STRUCTURAL ANALYSIS , 1989 .

[107]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[108]  Xavier Olive,et al.  Coupling Continuous and Discrete Event System Techniques for Hybrid System Diagnosability Analysis , 2008, ECAI.

[109]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

[110]  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).

[111]  Marcel Staroswiecki,et al.  Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems , 2001, Autom..

[112]  Malik Ghallab,et al.  Situation Recognition: Representation and Algorithms , 1993, IJCAI.

[113]  Brian C. Williams,et al.  Conflict-directed A* and its role in model-based embedded systems , 2007, Discret. Appl. Math..

[114]  Henry A. Kautz,et al.  Constraint Propagation Algorithms for Temporal Reasoning , 1986, AAAI.

[115]  George J. Vachtsevanos,et al.  A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .

[116]  John Langford,et al.  Risk Sensitive Particle Filters , 2001, NIPS.

[117]  Carine Jauberthie,et al.  Set-membership identifiability: definitions and analysis , 2011 .

[118]  Audine Subias,et al.  A discrete event model for situation awareness purposes , 2006 .

[119]  Jan Lunze,et al.  Handbook of hybrid systems control : theory, tools, applications , 2009 .

[120]  P. Breedveld,et al.  Bond Graph Modeling Procedures for Fault Detection and Isolation of Complex Flow Processes , 2001 .

[121]  Erann Gat,et al.  An Autonomous Spacecraft Agent Prototype , 1997, AGENTS '97.

[122]  Sheila A. McIlraith,et al.  Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net , 2002, UAI.

[123]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[124]  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).

[125]  Mitra Fouladirad,et al.  Optimal fault detection with nuisance parameters and a general covariance matrix , 2008 .