Sensor Fusion for Fault Detection and Classification in Distributed Physical Processes

This paper proposes a feature extraction and fusion methodology to perform fault detection & classification in distributed physical processes generating heterogeneous data. The underlying concept is built upon a semantic framework for multi-sensor data interpretation using graphical models of Probabilistic Finite State Automata (PFSA).While the computational complexity is reduced by pruning the fused graphical model using an information-theoretic approach, the algorithms are developed to achieve high reliability via retaining the essential spatiotemporal characteristics of the physical processes. The concept has been validated on a simulation test bed of distributed shipboard auxiliary systems.

[1]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[2]  Thomas M. Cover,et al.  Elements of information theory (2. ed.) , 2006 .

[3]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[4]  Asok Ray,et al.  Real-time estimation of lead-acid battery parameters: A dynamic data-driven approach , 2014 .

[5]  Visakan Kadirkamanathan,et al.  Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[6]  Asok Ray,et al.  Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns , 2008, 2008 American Control Conference.

[7]  Asok Ray,et al.  An inner product space on irreducible and synchronizable probabilistic finite state automata , 2012, Mathematics of Control, Signals, and Systems.

[8]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[9]  Tshilidzi Marwala,et al.  Hidden Markov Models and Gaussian Mixture Models for Bearing Fault Detection Using Fractals , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[10]  Kenji Yamanishi,et al.  Mining abnormal patterns from heterogeneous time‐series with irrelevant features for fault event detection , 2009, Stat. Anal. Data Min..

[11]  Asok Ray,et al.  Performance comparison of feature extraction algorithms for target detection and classification , 2013, Pattern Recognit. Lett..

[12]  Kushal Mukherjee,et al.  State splitting and merging in probabilistic finite state automata for signal representation and analysis , 2014, Signal Process..

[13]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[14]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[15]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[16]  Jacquelien M. A. Scherpen,et al.  Fault detection method for nonlinear systems based on probabilistic neural network filtering , 2002, Int. J. Syst. Sci..

[17]  Jovan D. Boskovic,et al.  A multiple model predictive scheme for fault-tolerant flight control design , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[18]  Christophe Andrieu,et al.  Particle methods for change detection, system identification, and control , 2004, Proceedings of the IEEE.

[19]  Luis M. de Campos,et al.  A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests , 2006, J. Mach. Learn. Res..

[20]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[21]  Cyrill Stachniss,et al.  Efficient information-theoretic graph pruning for graph-based SLAM with laser range finders , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Asok Ray,et al.  Symbolic dynamic analysis of complex systems for anomaly detection , 2004, Signal Process..

[23]  P. Frank,et al.  Deterministic nonlinear observer-based approaches to fault diagnosis: A survey , 1997 .

[24]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[25]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[26]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[27]  Asok Ray,et al.  Symbolic time series analysis via wavelet-based partitioning , 2006, Signal Process..

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

[29]  Brad Seanor,et al.  A fault tolerant flight control system for sensor and actuator failures using neural networks , 2000 .

[30]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .