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2014 | 24 | 2 | 271-282
Tytuł artykułu

Artificial intelligence methods in diagnostics of analog systems

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the state of the art and advancement of artificial intelligence methods in analog systems diagnostics. Firstly, the diagnostic domain is introduced and its problems explained. Then, computational intelligence approaches usable for fault detection and identification are reviewed. Particular groups of methods are presented in detail, explaining their usefulness and drawbacks. Examples, such as the induction motor or the electronic filter, are provided to show the applicability of the presented approaches for monitoring the state of analog objects from engineering domains. The discussion section reviews the presented approaches, their future prospects and problems to be solved.
Rocznik
Tom
24
Numer
2
Strony
271-282
Opis fizyczny
Daty
wydano
2014
otrzymano
2013-01-21
poprawiono
2013-02-28
Twórcy
autor
  • Institute of Radioelectronics, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
  • Department of Applied Informatics, Warsaw University of Life Sciences, ul. Nowoursynowska 159, 02-776 Warsaw, Poland
  • Institute of Radioelectronics, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
  • Aminian, F. and Aminian, M. (2001). Fault diagnosis of analog circuits using Bayesian neural networks with wavelet transform as preprocessor, Journal of Electronic Testing 17(1): 29-36.
  • Anand, G. (2012). Application of artificial neural networks in electrical machines: An overview, World Academy of Science, Engineering and Technology 6(6): 384-385.
  • Ben Hamida, N. and Kaminska, B. (1993). Multiple fault analog circuit testing by sensitivity analysis, Journal of Electronic Testing 4(4): 331-343.
  • Betta, G. and Pietrosanto, A. (2000). Instrument fault detection and isolation: State of the art and new research trends, IEEE Transactions on Instrumentation and Measurement 49(1): 100-107.
  • Bilski, P. and Wojciechowski, J. (2007). Automated diagnostics of analog systems using fuzzy logic approach, IEEE Transactions on Instrumentation Measurement 56(6): 2175-2185.
  • Bilski, P. and Wojciechowski, J. (2011). Rough-sets-based reduction for analog systems diagnostics, IEEE Transactions on Instrumentation and Measurement 60(3): 880-890.
  • Bilski, P. (2013). Application of clustering methods for the ambiguity groups detection in the diagnostic of analog systems, Przegląd Elektrotechniczny 89(2a): 276-278.
  • Bilski, P. and Wojciechowski, J. (2012). Current research trends in diagnostics of analog systems, Proceedings of the International Conference on Signals and Electronic Systems, Wrocław, Poland, DOI: 10.1109/TIM.2010.2060225.
  • Browning, T.R. (2001). Applying the design structure matrix to system decomposition and integration problems: A review and new directions, IEEE Transactions on Engineering Management 48(3): 292-306.
  • Czaja, Z. and Zielonko, R. (2003). Fault diagnosis in electronic circuits based on bilinear transformation in 3D and 4D spaces, IEEE Transactions on Instrumentation and Measurement 52(1): 97-102.
  • Karki, J. (2002). Active low-pass filter design, Texas Instruments, http://www.ti.com/lit/an/ sloa049b/sloa049b.pdf.
  • Maiden, Y., Jervis Barrie, W., Fouillat, P. and Lesage, S. (1999). Using artificial neural networks or Lagrange interpolation to characterize the faults in an analog circuit: An experimental study, IEEE Transactions on Instrumentation and Measurement 48(5): 932-938.
  • Nowicki, A., Grochowski, M. and Duzinkiewicz, K. (2012). Data-driven models for fault detection using kernel PCA: A water distribution system case study, International Journal of Applied Mathematics and Computer Science 22(4): 939-949, DOI: 10.2478/v10006-012-0070-1.
  • Patan, K., Witczak, M. and Korbicz, J. (2008). Towards robustness in neural network based fault diagnosis, International Journal of Applied Mathematics and Computer Science 18(4): 443-454, DOI: 10.2478/v10006-008-0039-2.
  • Pöyhönen, S., Negrea, M., Arkkio, A., Hyötyniemi, H. and Koivo, H. (2002). Fault diagnostics of an electrical machine with multiple support vector classifiers, Proceedings of the 17th IEEE International Symposium on Intelligent Control, Vancouver, British Columbia, Canada, Vol. 1, pp. 373-378.
  • Rutkowski, J. and Grzechca, D. (2001). Analog fault dictionary-fuzzy set approach, Proceedings of the European Conference on Circuit Theory and Design, Helsinki, Finland, pp. 253-256.
  • Rutkowski, J. and Zieliński, L. (2003). Using evolutionary techniques for chosen optimization problems related to analog circuits design, Proceedings of the 16th European Conference on Circuits Theory and Design, Cracow, Poland, Vol. 3, pp. III-313-316.
  • Sałat, R. and Osowski, S. (2011). Support vector machine for soft fault location in electrical circuits, Journal of Intelligent and Fuzzy Systems 22(1): 21-31.
  • Samanta, B. and Nataraj, C. (2009). Use of particle swarm optimization for machinery fault detection, Engineering Applications of Artificial Intelligence 22(2): 308-316.
  • Simani, S. (2013). Residual generator fuzzy identification for automotive diesel engine fault diagnosis, International Journal of Applied Mathematics and Computer Science 23(2): 419-438, DOI: 10.2478/amcs-2013-0032.
  • Smyth, P. (1994). Hidden Markov models for fault detection in dynamic systems, Pattern Recognition 27(1): 149-164.
  • Starzyk, J.A., Pang, J., Manetti, S., Piccirilli, M.C. and Fedi, G. (2000). Finding ambiguity groups in low testability analog circuits, IEEE Transactions on Circuits and Systems 47(8): 1125-1137.
  • Starzyk, J., Liu, D., Liu, Z., Nelson, D. and Rutkowski, J. (2004). Entropy-based optimum test points selection for analog fault dictionary techniques, IEEE Transactions on Instrumentation and Measurement 53(3): 754-761.
  • Svensson, M., Byttner, S. and Rognvaldsson, T. (2008). Self-organizing maps for automatic fault detection in a vehicle cooling system, Proceedings of the 4th International IEEE Conference on Intelligent Systems, Varna, Bulgaria, Vol. 3, pp. 8-12.
  • Tadeusiewicz, M. and Hałgas, S. (2007). Finding operating points of the diode-transistor circuits via homotopy approach, Przegląd Elektrotechniczny 83(2): 69-72.
  • Tadeusiewicz, M., Hałgas, S. and Korzybski, M. (2011). Multiple catastrophic fault diagnosis of analog circuits considering the component tolerances, International Journal on Circuits Theory and Applications 40(10): 1041-1052, DOI: 10.1002/cta.770.
  • Tudoroiu, N. and Zaheeruddin, M. (2005). Fault detection and diagnosis of valve actuators in HVAC systems, Proceedings of the 2005 IEEE Conference on Control Applications, Toronto, Canada, pp. 1281-1286.
  • Wang, K., Chiasson, J., Bodson, M. and Tolbert, L.M. (2007). An online rotor time constant estimator for the induction machine, IEEE Transactions on Control Systems Technology 15(2): 339-348.
  • Wu, S. and Chow, T.W.S. (2004). Induction machine fault detection using SOM-based RBF neural networks, IEEE Transactions on Industrial Electronics 51(1): 183-194.
  • Xue, H. and Jiang, J.G. (2006). Fault detection and accommodation for nonlinear systems using fuzzy neural networks, Proceedings of the 5th International Power Electronics and Motion Control Conference, Shanghai, China, pp. 1-5, DOI: 10.1109/IPEMC.2006.4778342.
  • Zhu, L., Zhu, Y., Mao, H. and Gu, M. (2009). A new method for sparse signal denoising based on compressed sensing, 2nd International Symposium on Knowledge Acquisition and Modeling, Wuhan, China, pp. 35-38.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv24i2p271bwm
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