A Self-Building and Cluster-Based Cognitive Fault Diagnosis System for Sensor Networks

Cognitive fault diagnosis systems differentiate from more traditional solutions by providing online strategies to create and update the fault-free and the faulty classes directly from incoming data. This aspect is of paramount relevance within the big data framework, since measurements are there immediately processed to detect and identify the upsurge of potential faults. This paper introduces a novel cognitive fault diagnosis framework for processes described by nonlinear dynamic systems that inspects changes in the existing relationships among sensors. The proposed framework is based on an evolving clustering algorithm that operates in the parameter space of time invariant linear models approximating such relationships. During the operational life, parameter vectors associated with models thought not to belong to the nominal state are either labeled as outlier or fault. New classes of faults, here considered to propagate to the model parameters according to an abrupt profile, are created online as they appear. At the same time, existing classes can merge, depending on the information content carried by incoming data.

[1]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[2]  R. Yager,et al.  Approximate Clustering Via the Mountain Method , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[3]  Darek Ceglarek,et al.  Sensor Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems With Distributed Sensing , 2000 .

[4]  Plamen Angelov,et al.  Evolving Intelligent Systems: Methodology and Applications , 2010 .

[5]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[6]  Cesare Alippi,et al.  A high-frequency sampling monitoring system for environmental and structural applications , 2013, TOSN.

[7]  Yu Ding,et al.  Optimal sensor distribution for variation diagnosis in multistation assembly processes , 2003, IEEE Trans. Robotics Autom..

[8]  Vicenç Puig,et al.  Efficient optimal sensor placement for model-based FDI using an incremental algorithm , 2007, 2007 46th IEEE Conference on Decision and Control.

[9]  Nikola Kasabov,et al.  ECM — A Novel On-line, Evolving Clustering Method and Its Applications , 2001 .

[10]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[11]  Cesare Alippi,et al.  A Cognitive Fault Diagnosis System for Distributed Sensor Networks , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Erik Frisk,et al.  Sensor Placement for Fault Diagnosis , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[13]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

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

[15]  Purushottam W. Laud,et al.  The mutltidimensional Kolmogorov goodness-of-fit test , 1980 .

[16]  Jie Zhang,et al.  On-line process fault diagnosis using fuzzy neural networks , 1994 .

[17]  Chee Peng Lim,et al.  Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[18]  N. Smirnov.  Table for Estimating the Goodness of Fit of Empirical Distributions , 1948 .

[19]  Hsing-Chia Kuo,et al.  A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems , 2004, Eng. Appl. Artif. Intell..

[20]  Olfa Nasraoui,et al.  Robust Clustering for Tracking Noisy Evolving Data Streams , 2006, SDM.

[21]  Piero P. Bonissone,et al.  Fuzzy logic for fault diagnosis , 1991, Other Conferences.

[22]  R. Naresh,et al.  An Integrated Neural Fuzzy Approach for Fault Diagnosis of Transformers , 2008, IEEE Transactions on Power Delivery.

[23]  L. Ljung,et al.  Asymptotic normality of prediction error estimators for approximate system models , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

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

[25]  Furong Li,et al.  A novel approach to fault diagnosis in multicircuit transmission lines using fuzzy ARTmap neural networks , 1999, IEEE Trans. Neural Networks.

[26]  Yann-Chang Huang,et al.  Evolving neural nets for fault diagnosis of power transformers , 2003 .

[27]  Hans-Jürgen Zimmermann,et al.  Automatic fault detection in gearboxes by dynamic fuzzy data analysis , 1999, Fuzzy Sets Syst..

[28]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[29]  B.D. Appleby,et al.  Using learning techniques to accommodate unanticipated faults , 1993, IEEE Control Systems.

[30]  Miin-Shen Yang,et al.  A similarity-based robust clustering method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Cesare Alippi,et al.  A "Learning from Models" Cognitive Fault Diagnosis System , 2012, ICANN.

[32]  Dingli Yu,et al.  Sensor fault diagnosis in a chemical process via RBF neural networks , 1999 .

[33]  Avrim Blum,et al.  Random Projection, Margins, Kernels, and Feature-Selection , 2005, SLSFS.

[34]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[35]  Philippe Pierre Pebay,et al.  Formulas for robust, one-pass parallel computation of covariances and arbitrary-order statistical moments. , 2008 .

[36]  Indre Zliobaite,et al.  Combining similarity in time and space for training set formation under concept drift , 2011, Intell. Data Anal..

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

[38]  Jung-Hua Wang,et al.  Self-organizing mountain method for clustering , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[39]  Vicenç Puig Cayuela,et al.  Development of a Simulation Environment for Water Drinking Networks: Application to the Validation of a Centralized MPC Controller for the Barcelona Case Study , 2009 .

[40]  Benjamin Schrauwen,et al.  An overview of reservoir computing: theory, applications and implementations , 2007, ESANN.

[41]  L. Ljung.  Convergence analysis of parametric identification methods , 1978 .

[42]  Janos Gertler,et al.  Fault Detection and Diagnosis , 2008, Encyclopedia of Systems and Control.

[43]  Bo-Suk Yang,et al.  Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis , 2004, Expert Syst. Appl..

[44]  Suphakant Phimoltares,et al.  A Very Fast Neural Learning for Classification Using Only New Incoming Datum , 2010, IEEE Transactions on Neural Networks.

[45]  Hong-Tzer Yang,et al.  Developing a new transformer fault diagnosis system through evolutionary fuzzy logic , 1997 .

[46]  David M. Rocke,et al.  Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator , 2004, Comput. Stat. Data Anal..

[47]  Herbert Jaeger,et al.  A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach - Semantic Scholar , 2005 .

[48]  Marios M. Polycarpou,et al.  Automated fault diagnosis in nonlinear multivariable systems using a learning methodology , 2000, IEEE Trans. Neural Networks Learn. Syst..

[49]  Mathew D. Penrose,et al.  Extremes for the minimal spanning tree on normally distributed points , 1998, Advances in Applied Probability.

[50]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[51]  Marios M. Polycarpou,et al.  Incipient fault diagnosis of dynamical systems using online approximators , 1998 .

[52]  AN Kolmogorov-Smirnov,et al.  Sulla determinazione empírica di uma legge di distribuzione , 1933 .