Artificial intelligence methods in diagnostics of analog systems

Aminian, F. and Aminian, M. (2001). Fault diagnosis of analog circuits using Bayesian neural networks with wavelet transform as preprocessor, Journal of Electronic Testing17(1): 29–36.Search in Google Scholar

Anand, G. (2012). Application of artificial neural networks in electrical machines: An overview, World Academy of Science, Engineering and Technology6(6): 384–385.Search in Google Scholar

Ben Hamida, N. and Kaminska, B. (1993). Multiple fault analog circuit testing by sensitivity analysis, Journal of Electronic Testing4(4): 331–343.10.1007/BF00972158Search in Google Scholar

Betta, G. and Pietrosanto, A. (2000). Instrument fault detection and isolation: State of the art and new research trends, IEEE Transactions on Instrumentation and Measurement49(1): 100–107.10.1109/19.836318Search in Google Scholar

Bilski, P. and Wojciechowski, J. (2007). Automated diagnostics of analog systems using fuzzy logic approach, IEEE Transactions on Instrumentation Measurement56(6): 2175–2185.10.1109/TIM.2007.908152Search in Google Scholar

Bilski, P. and Wojciechowski, J. (2011). Rough-sets-based reduction for analog systems diagnostics, IEEE Transactions on Instrumentation and Measurement60(3): 880–890.10.1109/TIM.2010.2060225Search in Google Scholar

Bilski, P. (2013). Application of clustering methods for the ambiguity groups detection in the diagnostic of analog systems, Przegla˛d Elektrotechniczny89(2a): 276–278.Search in Google Scholar

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.2010.2060225.10.1109/TIM.2010.2060225Search in Google Scholar

Browning, T.R. (2001). Applying the design structure matrix to system decomposition and integration problems: A review and new directions, IEEE Transactions on Engineering Management48(3): 292–306.10.1109/17.946528Search in Google Scholar

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 Measurement52(1): 97–102.10.1109/TIM.2003.809075Search in Google Scholar

Karki, J. (2002). Active low-pass filter design, Texas Instruments, http://www.ti.com/lit/an/sloa049b/sloa049b.pdf.Search in Google Scholar

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 Measurement48(5): 932–938.10.1109/19.799650Search in Google Scholar

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 Science22(4): 939–949, DOI: 10.2478/v10006-012-0070-1.10.2478/v10006-012-0070-1Search in Google Scholar

Patan, K., Witczak, M. and Korbicz, J. (2008). Towards robustness in neural network based fault diagnosis, International Journal of Applied Mathematics and Computer Science18(4): 443–454, DOI: 10.2478/v10006-008-0039-2.10.2478/v10006-008-0039-2Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

Sałat, R. and Osowski, S. (2011). Support vector machine for soft fault location in electrical circuits, Journal of Intelligent and Fuzzy Systems22(1): 21–31.10.3233/IFS-2010-0471Search in Google Scholar

Samanta, B. and Nataraj, C. (2009). Use of particle swarm optimization for machinery fault detection, Engineering Applications of Artificial Intelligence22(2): 308–316.10.1016/j.engappai.2008.07.006Search in Google Scholar

Simani, S. (2013). Residual generator fuzzy identification for automotive diesel engine fault diagnosis, International Journal of Applied Mathematics and Computer Science23(2): 419–438, DOI: 10.2478/amcs-2013-0032.10.2478/amcs-2013-0032Search in Google Scholar

Smyth, P. (1994). Hidden Markov models for fault detection in dynamic systems, Pattern Recognition27(1): 149–164.10.1016/0031-3203(94)90024-8Search in Google Scholar

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 Systems47(8): 1125–1137.10.1109/81.873868Search in Google Scholar

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 Measurement53(3): 754–761.10.1109/TIM.2004.827085Search in Google Scholar

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.Search in Google Scholar

Tadeusiewicz, M. and Hałgas, S. (2007). Finding operating points of the diode-transistor circuits via homotopy approach, Przegląd Elektrotechniczny83(2): 69–72.Search in Google Scholar

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 Applications40(10): 1041–1052, DOI: 10.1002/cta.770.10.1002/cta.770Search in Google Scholar

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.Search in Google Scholar

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 Technology15(2): 339–348.10.1109/TCST.2006.886445Search in Google Scholar

Wu, S. and Chow, T.W.S. (2004). Induction machine fault detection using SOM-based RBF neural networks, IEEE Transactions on Industrial Electronics51(1): 183–194.10.1109/TIE.2003.821897Search in Google Scholar

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.2006.4778342.10.1109/IPEMC.2006.4778342Search in Google Scholar

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.Search in Google Scholar