Artificial intelligence methods in diagnostics of analog systems
By: Piotr Bilski and Jacek Wojciechowski
References
- Aminian, F. and Aminian, M. (2001). Fault diagnosis of analog circuits using Bayesian neural networks with wavelet transform as preprocessor,(1): 29–36.
- Anand, G. (2012). Application of artificial neural networks in electrical machines: An overview,(6): 384–385.
- Ben Hamida, N. and Kaminska, B. (1993). Multiple fault analog circuit testing by sensitivity analysis,(4): 331–343.
- Betta, G. and Pietrosanto, A. (2000). Instrument fault detection and isolation: State of the art and new research trends,(1): 100–107.
- Bilski, P. and Wojciechowski, J. (2007). Automated diagnostics of analog systems using fuzzy logic approach,(6): 2175–2185.
- Bilski, P. and Wojciechowski, J. (2011). Rough-sets-based reduction for analog systems diagnostics,(3): 880–890.
- Bilski, P. (2013). Application of clustering methods for the ambiguity groups detection in the diagnostic of analog systems,(2a): 276–278.
- Bilski, P. and Wojciechowski, J. (2012). Current research trends in diagnostics of analog systems,, 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,(3): 292–306.
- Czaja, Z. and Zielonko, R. (2003). Fault diagnosis in electronic circuits based on bilinear transformation in 3D and 4D spaces,(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,(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,(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,(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,, Vol. 1, pp. 373–378.
- Rutkowski, J. and Grzechca, D. (2001). Analog fault dictionary—fuzzy set approach,, pp. 253–256.
- Rutkowski, J. and Zieliński, L. (2003). Using evolutionary techniques for chosen optimization problems related to analog circuits design,, Vol. 3, pp. III-313–316.
- Sałat, R. and Osowski, S. (2011). Support vector machine for soft fault location in electrical circuits,(1): 21–31.
- Samanta, B. and Nataraj, C. (2009). Use of particle swarm optimization for machinery fault detection,(2): 308–316.
- Simani, S. (2013). Residual generator fuzzy identification for automotive diesel engine fault diagnosis,(2): 419–438, DOI: 10.2478/amcs-2013-0032.
- Smyth, P. (1994). Hidden Markov models for fault detection in dynamic systems,(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,(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,(3): 754–761.
- Svensson, M., Byttner, S. and Rognvaldsson, T. (2008). Self-organizing maps for automatic fault detection in a vehicle cooling system,, Vol. 3, pp. 8–12.
- Tadeusiewicz, M. and Hałgas, S. (2007). Finding operating points of the diode-transistor circuits via homotopy approach,(2): 69–72.
- Tadeusiewicz, M., Hałgas, S. and Korzybski, M. (2011). Multiple catastrophic fault diagnosis of analog circuits considering the component tolerances,(10): 1041–1052, DOI: 10.1002/cta.770.
- Tudoroiu, N. and Zaheeruddin, M. (2005). Fault detection and diagnosis of valve actuators in HVAC systems,, pp. 1281–1286.
- Wang, K., Chiasson, J., Bodson, M. and Tolbert, L.M. (2007). An online rotor time constant estimator for the induction machine,(2): 339–348.
- Wu, S. and Chow, T.W.S. (2004). Induction machine fault detection using SOM-based RBF neural networks,(1): 183–194.
- Xue, H. and Jiang, J.G. (2006). Fault detection and accommodation for nonlinear systems using fuzzy neural networks,, 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,, pp. 35–38.
Language: English
Page range: 271 - 282
Submitted on: Jan 21, 2013
Published on: Jun 26, 2014
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2014 Piotr Bilski, Jacek Wojciechowski, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.