[1] M. Schickert, I. Aydin, M. Karak¨ose, E. & Akin, ”A new contactless fault diagnosis approach for pantograph-catenary system,” in MECHATRONIKA, 2012 15th International Symposium, pp. 1-6, IEEE, 2012.
[2] A. Le Mortellec, J. Clarhaut, Y. Sallez, D. Berger, & Trentesaux, ”Embedded holonic fault diagnosis of complex transportation systems,” Engineering Applications of Artificial Intelligence, 26(1), pp. 227-24, 2013.
[3] Ph M. Papaelias, C. Roberts, L.C. & Davis, ”A review on non-destructive evaluation of rails: state-of-the-art and future development,” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 222(4), pp. 367-384, 2008.
[4] J. Seeliger, Mackel, D. Georges, ”Measurement and diagnosis of process-disturbing oscillations in high-speed rolling plants,” in Proc. XIV IMEKO World Congress, 2002.
[5] X.S. Jin, J. Guo, X.B. Xiao, Z.F. Wen, & Z.R. Zhou, ”Key scientific problems in the study on running safety of high speed trains,” Engineering Mechanics, 26 (Sup II), pp. 8–25, 2009.
[6] L. Mariani, F. Pastore, & M. Pezze, ”Dynamic analysis for diagnosing integration faults,” IEEE Transactions on Software Engineering, 37(4), pp. 486-508, 2011.
[7] Y.C. Huang, H.C. Sun, ”Dissolved gas analysis of mineral oil for power transformer fault diagnosis using fuzzy logic,” IEEE Transactions on Dielectrics and Electrical Insulation, 20(3), pp. 974-981, 2013.
[8] Orb´an, Zolt´an, and Marc Gutermann, ”Assessment of masonry arch railway bridges using non-destructive in-situ testing methods,” Engineering Structures, 31.10 (2009): pp. 2287-2298, 2009.
[9] Yella, Siril, M. S. Dougherty, and N. K. Gupta, ”Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing,” Insight-Non-Destructive Testing and Condition Monitoring, 48.1: pp. 10-20, 2006.
[10] P.S. Bhowmik, S. Pradhan, M. Prakash, ”Fault diagnostic and monitoring methods of inductionmotor: a review,” International Journal of Applied Control, Electrical and Electronics, Vol. 1, pp. 1-18, 2013.
[11] Petr Dolezel, Pavel Skrabanek, Lumir Gago, ”Pattern recognition neural network as a tool for pest birds detection,” Computational Intelligence, SSCIIEEE Symposium Series on, pp. 1-6, 2016.
[12] V. Lakhno, Y. Tkach, T. Petrenko, S. Zaitsev, & V. Bazylevych, V. ”Development of adaptive expert system of information security using a procedure of clustering the attributes of anomalies and cyber attacks,” Eastern-European Journal of Enterprise Technologies, (6 (9)), pp. 32- 44, 2016.
[13] V. Lakhno, ”Creation of the adaptive cyber threat detection system on the basis of fuzzy feature clustering,” Eastern-European Journal of Enterprise Technologies, 2.9 2016:18, 2016.
[14] V.A. Lakhno, P.U. Kravchuk, V.P. Malyukov, V.N. Domrachev, L.V. Myrutenko, O.S. Piven, ”Developing of the cyber security system based on clustering and formation of control deviation signs,” Journal of Theoretical and Applied Information Technology, Vol. 95, Iss. 21, pp. 5778-5786.
[15] A.S. Dovbish, ”Osnovi proektuvannja ntelektualnih sistem” / A.S. Dovbish. Sumi: SumDU, 171 p., 2009.
[16] X. Zhang, N. Feng, Y. Wang, & Y. Shen, ”Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy,” Journal of Sound and Vibration, p. 419-432, 2015.
[17] A. Giantomassi, F. Ferracuti, S. Iarlori, G. Ippoliti, & S. Longhi, ”Electric motor fault detection and diagnosis by kernel density estimation and Kullback–Leibler divergence based on stator current measurements,” IEEE Transactions on Industrial Electronics, 2015, 62(3), pp. 1770- 1780, 2015.