FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a fuzzyneural data fusion engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms; 2) anomalous behavior detector using self-organizing fuzzy logic system; and 3) artificial neural network-based system modeling and prediction. The improved control system stateawareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network-based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory's hybrid energy system facility known as HYTEST. Experiment results demonstrate that the proposed FNDFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold-based alarm systems.

[1]  J. Michael Doster,et al.  Application of a neural network based feedwater controller to helical steam generators , 2009 .

[2]  W. J. Kim,et al.  Application of neural networks to signal prediction in nuclear power plant , 1993 .

[3]  Z. Guo,et al.  Nuclear power plant performance study by using neural networks , 1991, Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference.

[4]  Mark J. Embrechts,et al.  Hybrid identification of nuclear power plant transients with artificial neural networks , 2004, IEEE Transactions on Industrial Electronics.

[5]  Carl M. Stoots,et al.  Integrated Operation of the INL HYTEST System and High-Temperature Steam Electrolysis for Synthetic Natural Gas Production , 2012 .

[6]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[7]  David Wang,et al.  Robust Data-Driven Modeling Approach for Real-Time Final Product Quality Prediction in Batch Process Operation , 2011, IEEE Transactions on Industrial Informatics.

[8]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[9]  Milos Manic,et al.  Neural Network based Intrusion Detection System for critical infrastructures , 2009, 2009 International Joint Conference on Neural Networks.

[10]  Tharam S. Dillon,et al.  Modeling of a Liquid Epoxy Molding Process Using a Particle Swarm Optimization-Based Fuzzy Regression Approach , 2011, IEEE Trans. Ind. Informatics.

[11]  Chia-Feng Juang,et al.  A Type-2 Self-Organizing Neural Fuzzy System and Its FPGA Implementation , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Hans Kleine Büning,et al.  Identifying behavior models for process plants , 2011, ETFA2011.

[13]  Shu-Li Sun,et al.  Multisensor optimal information fusion input white noise deconvolution estimators , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Taekyoung Kwon,et al.  An Experimental Study of Hierarchical Intrusion Detection for Wireless Industrial Sensor Networks , 2010, IEEE Transactions on Industrial Informatics.

[15]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[16]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[17]  Ren Yu,et al.  An Online Fault Diagnosis Method for Nuclear Power Plant Based on Combined Artificial Neural Network , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[18]  David I. Gertman,et al.  Resilient control systems: Next generation design research , 2009, 2009 2nd Conference on Human System Interactions.

[19]  D. Kushner,et al.  The real story of stuxnet , 2013, IEEE Spectrum.

[20]  Belle R. Upadhyaya,et al.  Sensor validation for power plants using adaptive backpropagation neural network , 1990 .

[21]  Dennis M. Buede,et al.  Data Fusion and Decision Support for Command and Control , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[22]  Man Gyun Na,et al.  Prediction of major transient scenarios for severe accidents of nuclear power plants , 2004, IEEE Transactions on Nuclear Science.

[23]  Soon Heung Chang,et al.  Development of an on-line fuzzy expert system for integrated alarm processing in nuclear power plants , 1995 .

[24]  Pascal Poncelet,et al.  Fuzzy anomaly detection in monitoring sensor data , 2010, International Conference on Fuzzy Systems.

[25]  Chia-Feng Juang,et al.  Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control , 2009, IEEE Transactions on Industrial Electronics.

[26]  Milos Manic,et al.  Fuzzy logic based anomaly detection for embedded network security cyber sensor , 2011, 2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS).

[27]  David Shan-Hill Wong,et al.  Comparison of Embedded System Design for Industrial Applications , 2011, IEEE Transactions on Industrial Informatics.

[28]  Shun-Feng Su,et al.  Fuzzy hierarchical data fusion networks for terrain location identification problems , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  Sheue-Ling Hwang,et al.  Application control chart concepts of designing a pre-alarm system in the nuclear power plant control room , 2008 .

[30]  Craig G. Rieger,et al.  HYTEST Phase I Facility Commissioning and Modeling , 2009 .

[31]  Paul J. Werbos,et al.  The roots of backpropagation , 1994 .

[32]  Daswin De Silva,et al.  A Data Mining Framework for Electricity Consumption Analysis From Meter Data , 2011, IEEE Transactions on Industrial Informatics.

[33]  A.A. Safavi,et al.  Enhanced Neural Network Based Fault Detection of a VVER Nuclear Power Plant With the Aid of Principal Component Analysis , 2008, IEEE Transactions on Nuclear Science.

[34]  V. V. S. Sarma,et al.  Multisensor data fusion and decision support for airborne target identification , 1991, IEEE Trans. Syst. Man Cybern..

[35]  Igor Nai Fovino,et al.  A Multidimensional Critical State Analysis for Detecting Intrusions in SCADA Systems , 2011, IEEE Transactions on Industrial Informatics.

[36]  Karim Salahshoor,et al.  Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers , 2010 .

[37]  Kamal Hadad,et al.  Fault diagnosis and classification based on wavelet transform and neural network , 2011 .

[38]  K. Nabeshima,et al.  Nuclear reactor monitoring with the combination of neural network and expert system , 2002, Math. Comput. Simul..

[39]  Mauro S. Tonelli-Neto,et al.  Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory , 2012 .

[40]  Thomas Palmé,et al.  Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant , 2010 .

[41]  Milos Manic,et al.  Anomaly detection for resilient control systems using fuzzy-neural data fusion engine , 2011, 2011 4th International Symposium on Resilient Control Systems.

[42]  Vasile Palade,et al.  Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks , 2009, Neurocomputing.