A Particle-Filtering Based Approach for Distributed Fault Diagnosis of Large-Scale Interconnected Nonlinear Systems

This paper deals with the problem of designing a distributed fault detection and isolation algorithm for nonlinear large-scale systems that are subjected to multiple fault modes. To solve this problem, a network of communicating detection nodes is deployed to monitor the monolithic process. Each node consists of an estimator with partial observation of the system's state. The local estimator executes a distributed variation of the particle filtering algorithm using the partial sensor measurements and the fault progression model of the process. During the implementation of the algorithm, each node communicates with its neighbors by sharing pre-processed information. The communication topology is defined using graph theoretic tools. The information fusion between the neighboring nodes is performed by means of a distributed average consensus algorithm to ensure the agreement over the value of the local likelihood functions. The proposed method enables online hypothesis testing without the need of a bank of estimators. Numerical simulations demonstrate the efficiency of the proposed approach.

[1]  George Vachtsevanos,et al.  An Adaptive Particle Filtering-based Framework for Real-time Fault Diagnosis and Failure Prognosis of Environmental Control Systems , 2011 .

[2]  Qi Cheng,et al.  Distributed Fault Detection with Correlated Decision Fusion , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Parameswaran Ramanathan,et al.  Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[4]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[5]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[6]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[7]  R. N. Claek.  Instrument Fault Detection , 1978 .

[8]  Allan J. Volponi,et al.  The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study , 2000 .

[9]  Yu Hen Hu,et al.  Distributed particle filters for wireless sensor network target tracking , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

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

[11]  Marios M. Polycarpou,et al.  Robust fault isolation for a class of non-linear input?output systems , 2001 .

[12]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[13]  Tung Le,et al.  Graphical Inference Methods for Fault Diagnosis based on Information from Unreliable Sensors , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[14]  Alan S. Willsky.  Detection of abrupt changes in dynamic systems , 1985 .

[15]  Marcos Eduardo Orchard,et al.  A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis , 2007 .

[16]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[17]  Karim Salahshoor,et al.  Centralized and decentralized process and sensor fault monitoring using data fusion based on adaptive extended Kalman filter algorithm , 2008 .

[18]  Donald L. Simon,et al.  Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics , 2003 .

[19]  Stephen P. Boyd,et al.  Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[20]  Petar M. Djuric,et al.  Likelihood Consensus and Its Application to Distributed Particle Filtering , 2011, IEEE Transactions on Signal Processing.

[21]  Rolf Isermann,et al.  Fault-Diagnosis Systems , 2005 .

[22]  Bin Jiang,et al.  Fault diagnosis based on adaptive observer for a class of non-linear systems with unknown parameters , 2004 .

[23]  Christopher Edwards,et al.  Robust decentralized actuator fault detection and estimation for large-scale systems using a sliding mode observer , 2008, Int. J. Control.

[24]  P.K. Varshney,et al.  Distributed fault detection via particle filtering and decision fusion , 2005, 2005 7th International Conference on Information Fusion.

[25]  Qi Zhang,et al.  Distributed fault diagnosis in a class of interconnected nonlinear uncertain systems , 2012, Int. J. Control.

[26]  Marios M. Polycarpou,et al.  A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems , 2002, IEEE Trans. Autom. Control..

[27]  Heidar Ali Talebi,et al.  Distributed Fault Detection and Isolation Filter Design for a Network of Heterogeneous Multiagent Systems , 2014, IEEE Transactions on Control Systems Technology.

[28]  D. Luenberger.  Observers for multivariable systems , 1966 .

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

[30]  Stephen P. Boyd,et al.  A scheme for robust distributed sensor fusion based on average consensus , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[31]  Reza Olfati-Saber,et al.  Distributed Kalman filtering for sensor networks , 2007, 2007 46th IEEE Conference on Decision and Control.

[32]  Karim Salahshoor,et al.  Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller , 2012 .

[33]  Richard Vernon Beard,et al.  Failure accomodation in linear systems through self-reorganization. , 1971 .

[34]  Michel Kinnaert,et al.  Robust fault detection based on observers for bilinear systems , 1999, Autom..

[35]  D. Luenberger.  Observing the State of a Linear System , 1964, IEEE Transactions on Military Electronics.

[36]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[37]  Gautam Biswas,et al.  Distributed Diagnosis in Formations of Mobile Robots , 2007, IEEE Transactions on Robotics.

[38]  Karl Henrik Johansson,et al.  Distributed fault detection for interconnected second-order systems , 2011, Autom..

[39]  Nicholas G. Polson,et al.  Particle Filtering , 2006 .

[40]  Mehrdad Saif,et al.  A sliding mode observer-based strategy for fault detection, isolation, and estimation in a class of Lipschitz nonlinear systems , 2007, Int. J. Syst. Sci..

[41]  Xidong Tang,et al.  Adaptive actuator failure compensation for nonlinear MIMO systems with an aircraft control application , 2007, Autom..

[42]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[43]  Michael G. Rabbat,et al.  Distributed auxiliary particle filters using selective gossip , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[44]  Stergios I. Roumeliotis,et al.  Set-Membership Constrained Particle Filter: Distributed Adaptation for Sensor Networks , 2011, IEEE Transactions on Signal Processing.

[45]  Dongbing Gu,et al.  Consensus based distributed particle filter in sensor networks , 2008, 2008 International Conference on Information and Automation.

[46]  Sheng Ma,et al.  Adaptive diagnosis in distributed systems , 2005, IEEE Transactions on Neural Networks.

[47]  Qinghua Zhang,et al.  Nonlinear system fault diagnosis based on adaptive estimation , 2004, Autom..

[48]  Ondrej Hlinka,et al.  Distributed Particle Filtering in Networks of Agents , 2012 .

[49]  Mark Coates,et al.  Distributed particle filters for sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[50]  Dongbing Gu.  Distributed Particle Filter for Target Tracking , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[51]  Petar M. Djuric,et al.  Distributed particle filtering in agent networks: A survey, classification, and comparison , 2013, IEEE Signal Processing Magazine.

[52]  George J. Vachtsevanos,et al.  A Particle Filtering-based Framework for Real-time Fault Diagnosis of Autonomous Vehicles , 2013 .

[53]  S. Fabri,et al.  A sequential Monte Carlo filtering approach to fault detection and isolation in nonlinear systems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[54]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2004, IEEE Transactions on Automatic Control.

[55]  M. Twardy,et al.  Observer-based fault detection in dynamical systems - part II , 2004 .

[56]  R. Olfati-Saber,et al.  Distributed Fault Diagnosis using Sensor Networks and Consensus-based Filters , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[57]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[58]  Reza Olfati-Saber,et al.  Kalman-Consensus Filter : Optimality, stability, and performance , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[59]  Marios M. Polycarpou,et al.  Distributed Fault Detection and Isolation of Large-Scale Discrete-Time Nonlinear Systems: An Adaptive Approximation Approach , 2012, IEEE Transactions on Automatic Control.

[60]  Visakan Kadirkamanathan,et al.  Particle filtering-based fault detection in non-linear stochastic systems , 2002, Int. J. Syst. Sci..

[61]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[62]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[63]  Harold Lee Jones,et al.  Failure detection in linear systems , 1973 .

[64]  Hong Wang,et al.  Design of fault diagnosis filters and fault-tolerant control for a class of nonlinear systems , 2001, IEEE Trans. Autom. Control..

[65]  Marios M. Polycarpou,et al.  Distributed fault diagnosis for continuous-time nonlinear systems: The input-output case , 2013, Annu. Rev. Control..

[66]  Marios M. Polycarpou,et al.  A distributed fault detection methodology for a class of large-scale uncertain input-output discrete-time nonlinear systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

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

[68]  R. K. Mehra,et al.  Correspondence item: An innovations approach to fault detection and diagnosis in dynamic systems , 1971 .

[69]  Gianfranco Lamperti,et al.  Diagnosis of Large Active Systems , 1999, Artif. Intell..

[70]  Amir Asif,et al.  A consensus/fusion based distributed implementation of the particle filter , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[71]  Petar M. Djuric,et al.  Likelihood consensus: Principles and application to distributed particle filtering , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[72]  A. Willsky,et al.  A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems , 1976 .

[73]  Alberto Isidori,et al.  A Geometric Approach to Nonlinear Fault Detection and Isolation , 2000 .

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

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