INFORMATION-EXTREMAL CLUSTER-ANALYSIS OF INPUT DATA IN FUNCTIONAL DIAGNOSIS
Abstract
Keywords
Full Text:
PDF (Українська)References
Popov, Yu. V, Kudryavtsev, S. V., Stepanov, S. V. Modernizatsiya tormoznoy sistemy shakhtnoy pod"emnoy ustanovki [Modernization of the brake system of mine lifting installation] Gornyj informacionno-analiticheskij bjulleten' (nauchno-tehnicheskij zhurnal). – Mining information and analytical bulletin (scientific and technical journal), 2015, vol. 9, pp. 195-197.
Gerike, B. L., Gerike, P. B., Shahmanov, V. N. Dinamicheskaja diagnostika mashinnyh agregatov gornogo oborudovanija [Dynamic diagnostics of machine units of mining equipment]. Gornyj informacionno-analiticheskij bjulleten' (nauchno-tehnicheskij zhurnal) – Mining information and analytical bulletin (scientific and technical journal), 2011, no. 5, pp. 80–89.
Sun, W., Yang, G. A., Chen, Q., Palazoglu, A., Feng, K. Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation. Journal of Vibration and Control, 2013, vol. 19, no. 6, pp. 924-941. DOI: 10.1177/1077546311435348.
Lytvyn, O. M., Lobanova, L. S., Zaluzhna, H. V. Pro odyn pidkhid do testuvannya novykh metodiv rozv"yazannya nestatsionarnoyi zadachi teploprovidnosti [About one week before testing new methods for developing unsteady tasks and heating problems]. Shtuchnyy intelekt – Artificial intelligence, 2012, no. 1, pp. 219-228.
Dong, S., Xu, X., Liu, J., Gao, Z. Rotating Machine Fault Diagnosis Based on Locality Preserving Projection and Back Propagation Neural Network–Support Vector Machine Model. Measurement and Control, 2015, vol. 48, no. 7, pp. 211–216.
Wang, D., Tse, P. W., Tsui, K. L. An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process, 2013, vol. 35, no. 1, pp. 176-199.
Feng, Z., Chen, X., Liang, M. Joint envelope and frequency order spectrum analysis based on iterative generalized demodulation for planetary gearbox fault diagnosis under nonstationary conditions. Mech. Syst. Signal Process., 2016, vol. 76, pp. 242–264.
Andrzej, O., Zygmunt, S. Modern Methods Control and Diagnostics of Hoisting-Machines with Application of Artificial Intelligence Methods. Archives of Mining Sciences, 2010, no. 1, pp. 217 – 231.
Sidorenko, V. N., Chernyi, A. P. Monitoring of powerful electric drives on the basis of Data Mining technology]. Elektrotehnichni ta komp’yuterni sistemi – Electrical and computer systems, 2011, no. 3, pp. 313 -316.
Jayaswal, P., Verma, S. N., Wadhwani, A. K. Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis. Journal of Vibration and Control, 2011, vol. 17, no. 8, pp. 1131–1148.
Samanta, B, Al-Balushi, K. R. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical systems and signal processing, 2003, vol. 17, no. 2, pp. 317-328.
Subbotin, S. The neuro-fuzzy network synthesis and simplification on precedents in problems of diagnosis and pattern recognition. Optical Memory and Neural Networks (Information Optics), 2013, vol. 22, no. 2, pp. 97-103. DOI: 10.3103/s1060992x13020082.
Dovbysh, A. S.,. Budnyk, M. M., Moskalenko, V. V. Information Extreme Algorithm for Optimizing Parameters of Hyperellipsoidal Containers of Recognition Classes. Journal of automation and information sciences, 2012, vol. 44, no. 10, pp. 35 - 44. DOI: 10.1615/JAutomatInfScien.v44.i10.30.
Dovbysh, A., Velikodnyi, D., Shelehov, I., Bibyk, M. Information-extreme machine learning of the control system over the pover unit of a therminal pover main line. Eastern-European Journal of Enterprise Technologies, 2017, vol. 5/4, no. 89, pp. 17-24. DOI: 10.15587/1729-4061.2017.112121.
Dovbysh, A. S., Zimovets, V. I., Kozlov, Z. S. Information extreme learning algorithm system diagnostics about mine lifting machine. Radioelektronika i informatika, 2017, no. 4, pp. 58-63.
Scherbakova, G. Yu. DN-distribution Parameters Evaluation with Noise Stability Clustering for the Automated Systems of Technical Diagnostics. Shtuchnyy intelekt – Artificial Intelligence, 2010, vol. 4, pp. 491-496.
Schonlau M. Visualizing non-hierarchical and hierarchical cluster analyses with clustergrams. Computational Statistics, 2004, vol. 19, no 1, pp 95-111.
DOI: https://doi.org/10.32620/reks.2019.4.12
Refbacks
- There are currently no refbacks.