10.5. Summary
This chapter is concerned with the application of fuzzy neural networks to fault detection and isolation systems. Thus, for readers not familiar with the subject, the background knowledge associated with artificial neural networks and the potential fields of application of this technology is presented in the introduction section. Furthermore, aiming to demonstrate that such a technology is mature enough to be applied in the solution of several kinds of industrial problems, a wide range of industrial applications of classical feedforward artificial neural networks are also reported in section 10.2, as well as applications of different types of fuzzy neural networks.
Section 10.3 is concerned with the development of FDI approaches based on fuzzy neural networks and a specific fault isolation system based on a hierarchical structure of several fuzzy neural networks is described in detail. The robustness and performance of such a fault isolation system has been assessed in section 10.4 by using a test bed consisting of a pneumatic servomotor actuated industrial control valve. Different kinds of faults have been considered, which has been assumed to occur in an abrupt or incipient manner, or by affecting the measurement variables in the process under supervision in an abrupt way or, instead, by affecting the process behaviour slowly (incipient faults).
The results presented in section 10.4 have shown that, under abrupt faults, the HSFNN provides very accurate results and is characterized by a good generalization capability as a fault isolation system. Under incipient or multiple simultaneous faulty scenarios, the performance of the proposed methodology depends on the fault development speed and/or on the system nonlinearities.
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References
AlguÍndigue IE and Uhrig RE (1994) Automatic fault recognition in mechanical components using coupled artificial neural networks. In: Proceedings of IEEE World Congress on Computational Intelligence, June–July, pp. 3312–3317
Andersen K, Cook GE, Karsai G and Ramaswamy K (1990) Artificial neural networks applied to arc welding process modeling and control. IEEE Transactions on Industry Applications 26:824–830
Anderson JA (1995) An Introduction to Neural Networks. MIT Press
Alport M, Mhlongo A, Naicker J and Plumb S (2002) Application of Neural Networks to Solve Industrial Problems: Bridging in Practice. Physica Scripta T97:118–121
Bezdek JC (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York
Bhat NV, Minderman PA, McAvoy T and Wang NS (1990) Modeling chemical process systems via neural computation. IEEE Control Systems Magazine 10:24–30
Bishop CM (1995) Neural Networks for Pattern Recognition. Oxford University Press.
Buckley JJ and Hayashi Y (1994a) Fuzzy Neural Networks. In: Yager RR and Zadeh LA (eds) Fuzzy Sets, Neural Networks, and Soft Computing. Van Nostrand Reinhold, New York, pp. 233–249
Buckley JJ and Hayashi Y (1994b) Can fuzzy neural nets approximate continuous functions, Fuzzy Sets and Systems 61(1):43–51
Buhl M and Lorenz RD (1991) Design and implementation of neural networks for digital current regulation of inverter drives. In: Proceedings of Conf. Rec. IEEE-IAS Annual Meeting, pp. 415–423
Burton B and Harley RG (1998) Reducing the computational demands of continually online-trained artificial neural networks for system identification and control of fast processes. IEEE Trans. on Industry Applications 34:589–596
Burton B, Kamran F, Harley RG, Habetler TG, Brooke M and Poddar R (1995) Identification and control of induction motor stator currents using fast on-line random training of a neural network. In: Proceedings of Conf. Rec. IEEE-IAS Annual Meeting, pp. 1781–1787
Boger Z (1995) Experience in developing models of industrial plants by large scale artificial neural networks. In: Proceedings of the Second New Zealand International Two-Stream Conf. Artificial Neural Networks and Expert Systems, pp. 326–329
Calado JMF and Sa da Costa JMG (1999) An Expert System Coupled with a Hierarchical Fuzzy Neural Network Approach for Multiple Fault Diagnosis. International Journal of Applied Mathematics and Computer Sciences 9(3): 667–687
Calado JMF, Korbicz J, Patan K, Patton RJ and Sa da Costa JMG (2001) Soft computing approaches to fault diagnosis for dynamic systems. European Journal of Control 7(2–3):169–208
Calado JMF, Carreira FPNF, Mendes MJGC, Sa da Costa JMG and Bartys M (2003b) Fault Detection Approach Based on Fuzzy Qualitative Reasoning Applied to the DAMADICS Benchmark Problem. In: Proceedings of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS’2003, Washington, D. C., USA, June 9–11, pp. 1179–1184
Carelli R, Camacho EF and Patiño D (1995) A neural network based feed forward adaptive controller for robots. IEEE Trans. on Systems, Man and Cybernetics 25: 1281–1288
Cichowlas M, Sobczuk D, Kazmierkowski MP and Malinowski M (2000) Novel artificial neural network based current controller for PWM rectifiers. In: Proceedings of the 9th Int. Conf. on Power Electronics and Motion Control, pp. 41–46
Chakraborty S, Pal K and Pal NR (2002) A neuro-fuzzy framework for inference. Neural Networks 15:247–261
Chen J (1995) Robust Residual Generation for Model-Based Fault Diagnosis of Dynamic Systems. PhD Thesis, Department of Electronics, University of York, UK
Chen J and Patton RJ (1999) Robust Model Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers, New York
Chen YC and Teng CC (1995) A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems 73:291–312
Cherian RP, Smith LN and Midha PS (2000) A neural network approach for selection of powder metallurgy materials and process parameters. Artificial Intelligence Engineering 14:39–44
Chow MY, Mangum PM and Yee SO (1991) A neural network approach to real-time condition monitoring of induction motors. IEEE Trans. ion Industrial Electronics 38:448–453
Chow MY, Sharpe RN and Hung JC (1993) On the application and design of artificial neural networks for motor fault detection—Part II. IEEE Trans. on Industrial Electronics 40: 189–196
Cook GE, Barnett RJ, Andersen K and Strauss AM (1995) Weld modelling and control using artificial neural network. IEEE Trans. on Industry Applications 31:1484–1491
Edwards PJ, Murray AF, Papadopoulos G, Wallace AR, Barnard J and Smith G (1999) The application of neural networks to the papermaking industry. IEEE Trans. on Neural Networks 10: 1456–1464
Er MJ and Liew KC (1997) Control of adept one SCARA robot using neural networks. IEEE Trans. on Industrial Electronics 44: 762–768
Farag WA, Quintana VH and Torres GL (1998) A genetic-based neuro-fuzzy approach for modelling and control of dynamical systems. IEEE Trans. on Neural Networks 9: 756–767
Feuring T, Buckley JJ and Hayashi Y (1999) Fuzzy neural nets can solve the overfitting problem. In: Proceedings of the Int. Joint Conference on Neural Networks 4: 4197–4201
Filippetti F, Franceschini G and Tassoni C (1995) Neural networks aided online diagnostics of induction motor rotor faults. IEEE Trans. on Industry Applications 31:892–899
Filippetti F, Franceschini G, Tassoni C and Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans. on Industrial Electronics 47: 994–1004
Fisher Controls, Control Valve Engineering. USA
Fogel DB (1990) Selecting an optimal neural network industrial electronics society. In: Proceedings of IEEE IECON’90,vol. 2, pp. 1211–1214
Fukuda T and Shibata T (1992) Theory and applications of neural networks for industrial control systems. IEEE Trans. on Industrial Applications 39: 472–489
Garcia FJ, Izquierdo V, Miguel L and Peran J (1997) Fuzzy Identification of Systems and its Applications to Fault Diagnosis Systems. In: Proceedings of the 3rd IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes — SAFEPROCESS’97, Hull, UK, vol. 2, August 26–28, pp. 705–712
Gorni AA (1997) The application of neural networks in the modeling of plate rolling processes. JOM-e 49(4) (electronic document)
Hashimoto H, Kubota T, Sato M and Harashima F (1992) Visual control of robotic manipulator based on neural networks. IEEE Trans. on Industrial Electronics 39: 490–496
Hayashi Y, Buckley JJ and Czogala E (1993) Fuzzy Neural Networks with Fuzzy Signals and Weights. International Journal of Intelligent Systems 8: 527–537
Haykin S (1999) Neural Networks — A Comprehensive Foundation (2nd Edition). Prentice-Hall, New Jersey
Horikawa S, Furuhashi T and Uchikawa Y (1992) On fuzzy modelling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans. on Neural Networks 3:801–806
Huang CY, Chen TC and Huang CL (1999) Robust control of induction motor with a neural-network load torque estimator and a neural-network identification. IEEE Trans. on Industrial Electronics 46:990–998
Ikonomopoulos A, Uhrig RE and Tsoukalas LH (1992) Use of neural networks to monitor power plant components. In: Proceedings of American Power Conference, vol. 54?II, April, pp. 1132–1137
Ishibuchi H, Morioka K and Turksen IB (1995) Learning by fuzzified neural networks. International Journal of Approximate Reasoning 13(3):327–358
Ishibuchi H and Nii M (2001) Numerical analysis of the learning of fuzzified neural networks from if-then rules. Fuzzy Sets and Systems 120(2):281–307
Jang J-SR (1993) ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. on Systems, Man and Cybernetics 23:665–684
Jung S and Hsia TC (1998) Neural network impedance force control of robot manipulator. IEEE Trans. on Industrial Electronics 45:451–461
Kavaklioglu K and Upadhyaya BR (1994) Monitoring feedwater flow rate and component thermal performance of pressurized water reactors by means of artificial neural networks. Nuclear Technology 107:112–123
Keller JM and Hunt D (1985) Incorporating Fuzzy Membership Functions into the Perceptron Algorithm. IEEE Trans. on Pattern Analysis and Machine Intelligence 7: 693–699
Khalid M and Omatu S (1992) A neural network controller for a temperature control system. IEEE Control Systems Magazine 12:58–64
Khalid M, Omatu S and Yusof R (1995) Temperature regulation with neural networks and alternative control schemes. IEEE Trans. on Neural Networks 6:572–582
Khotanzad A, Elragal H and Lu TL (2000) Combination of artificial neural-network forecasters for prediction of natural gas consumption. IEEE Trans. on Neural Networks 11:464–473
Kiguchi K and Fukuda T (1997) Intelligent position/force controller for industrial robot manipulators — application of fuzzy neural networks. IEEE Trans. on Industrial Electronics 44:753–761
Klir JG and Folger AT (1988) Fuzzy Sets, Uncertainty and Information. Prentice-Hall, New York
Koj J (1998) The Fault Sources of Pneumatic Servo-Motor-Control Valve Assembly. In: Proceedings of the III Polish National conference on Diagnosis of Industrial Processes, Jurata, Poland, pp. 415–419 (in Polish)
Koscielny JM and Syfert M (2000) Application of Fuzzy Neural Networks for Fault Isolation-Example for Power Boiler System. In: Proceedings of 6th IEEE International Conference on Methods and Models in Automation and Robotics-MMAR’2000, Miedzyzdroje, Poland, vol. 2, pp. 801–806
Kowal M, Korbicz J, Mendes MJGC and Calado JMF (2002) Fault Detection Using Neuro-Fuzzy Networks. Systems Science Journal 28(1):45–57
Lampinen J, Smolander S and Korhonen M (1998) Wood surface inspection system based on generic visual features. In: Fogelman-Soulié F and Gallinari P (eds) Industrial Applications of Neural Networks. World Scientific, pp. 35–42
Lee SC and Lee ET (1975) Fuzzy neural networks. Mathematical Biosciences 23:151–177
Leonhardt S and Ayoubi M (1997) Methods of fault diagnosis. Control Engineering Practice 5(5):683–692
Lewicki P, Hill T and Czyzewska M (1992) Nonconscious Acquisition of Information. American Psychologist 47(6):796–801
Lin C-T and Lee CSG (1996) Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice-Hall, Upper Saddle River, NJ
Lin FJ, Hwang WJ and Wai RJ (1999) A supervisory fuzzy neural network control system for tracking periodic inputs. IEEE Trans. on Fuzzy Systems 7:41–52
Lin FJ, Wai RJ and Hong CM (2001) Hybrid Supervisory Control Using Recurrent Fuzzy Neural Network for Tracking Periodic Inputs. IEEE Trans. on Neural Networks 12(1):68–90
Liu P (2000) On the approximation realization of fuzzy closure mapping by multilayer regular fuzzy neural network. Multiple Valued Logic 5(2): 463–480
Liu P and Wang H (1999) Research on approximation capability of regular fuzzy neural network to continuous fuzzy function. Science in China, Series E 41(2):143–151
Liu Y, Upadhyaya BR and Naghedolfeizi M (1993) Chemometric data analysis using artificial neural networks. Applied Spectroscopy. 47(1):12–23
Lopez-Toribio C, Patton R and Uppal F (1999) Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems. International Journal of Applied Mathematics and Computer Science 9(3):471–518
Louro R (2003) Fault Diagnosis of an Industrial Actuator Valve. MSc Dissertation, Instituto Superior Técnico, Technical University of Lisbon, November
Marquardt D (1963) An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM Journal ofn Applied Mathematics 11:164–168
Martineau S, Gaura E, Burnham KJ and Haas OCL (2002) Neural network control approach for an industrial furnace. In: Proceedings of the 14th International Conference on Systems Science, Las Vegas, USA, pp. 227–233
McCulloch WS and Pitts W (1943) A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematics and Biophysics 5:115–133
Mendes MJGC, Kowal M, Calado JMF, Korbicz J and Sa da Costa JMG (2002) Fault Isolation Approach Using a Profibus Network: a case study. In: CONTROLO’2002, 5th Portuguese Conference on Automatic Control, pp. 525–530
Naidu SR, Zafiriou E and McAvoy TJ (1990) Use of neural networks for sensor failure detection in a control system. IEEE Control Systems Magazine 10:49–55
Ozaki T, Suzuki T, Furuhashi T, Okuma S and Uchikawa Y (1991) Trajectory control of robotic manipulators using neural networks. IEEE Trans. on Industrial Electronics 38
Patterson DW (1996) Artificial Neural Networks: Theory and Applications. Prentice-Hall
Patton RJ, Lopez-Toribio CJ and Uppal FJ (1999) Artificial Intelligence Approaches to Fault Diagnosis. International Journal of Applied Mathematics and Computer Sciences 9(3):471–518
Patton RJ, Frank PM and Clark RN (2000) Issues of Fault Diagnosis for Dynamic Systems. Springer, London
Payeur P, Le-Huy H and Gosselin CM (1995) Trajectory prediction for moving objects using artificial neural networks. IEEE Trans. on Industrial Electronics 42:147–158
Rahman MH, Fazlur R, Devanathan R and Kuanyi Z (2000) Neural network approach for linearizing control of nonlinear process plants. IEEE Trans. on Industrial Electronics 47:470–477
Rosenblatt F (1958) The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65:386–408
Rubaai A and Kotaru R (2000) Online identification and control of a DC motor using learning adaptation of neural networks. IEEE Trans. on Industry Applications 36:935–942
Rumelhart DE, Widrow B and Lehr MA (1994) The basic ideas in neural networks. Communications of the ACM 37(3):87–92
Rutkowska D and Hayashi Y (1999) Neuro-Fuzzy Systems Approaches. Journal of Advanced Computational Intelligence 3(3):177–185
Sardy S, Ibrahim L and Yasuda Y (1993) An application of vision system for the identification and defect detection on woven fabrics by using artificial neural networks. In: Proceedings of Int. Joint Conference on Neural Networks, pp. 2141–2144
Sorsa T, Koivo HN and Koivisto H (1991) Neural networks in process fault diagnosis. IEEE Trans. on Systems, Man and Cybernetics 21:815–825
Sun F, Sun Z and Woo PY (2001) Neural network-based adaptive controller design of robotic manipulators with an observer. IEEE Trans. on Neural Networks 12:54–67
Sundareshan MK and Askew C (1997) Neural network-assisted variable structure control scheme for control of a flexible manipulator arm. Automatica 33(9):1699–1710
Takagi H (1990) Fusion technology of fuzzy theory and neural networks — Survey and future directions. In: Proceedings of International Conference on Fuzzy Logic and Neural Networks (IIZUKA’90), Iizuka, Japan, July 20–24, pp. 13–26
Tsoukalas L and Reyes-Jimenez J (1990) Hybrid expert system-neural network methodology for nuclear plant monitoring and diagnostics. In: Proceedings of SPIE Applications of Artificial Intelligence VIII, vol. 1293, April 1990, pp. 1024–1030
Uhrig RE (1994) Application of artificial neural networks in industrial technology. In: Proceedings of the IEEE Int. Conf. Industrial Technology, pp.73–77
Vemuri AT and Polycarpou MM (1997) Neural-network-based robust fault diagnosis in robotic systems. IEEE Trans. on Neural Networks 8:1410–1420
Venayagamoorthy GK and Harley RG (1999) Experimental studies with a continually online-trained artificial neural network controller for a turbo generator. In: Proceedings of the International Joint Conference on Neural Networks, vol. 3, Washington, DC, July, pp. 2158–2163
Zurada JM (1992) Introduction to Artificial Neural Systems. West Publishing Company
Wang LX (1997) A Course in Fuzzy Systems and Control. Prentice-Hall, Englewood Cliffs, NJ
Wishart M and Harley RG (1995) Identification and control of induction machines using artificial neural networks. IEEE Trans. on Industry Applications 31:612–619
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Calado, J., Sá da Costa, J. (2006). Fuzzy Neural Networks Applied to Fault Diagnosis. In: Palade, V., Jain, L., Bocaniala, C.D. (eds) Computational Intelligence in Fault Diagnosis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-631-5_10
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