Recognition of Thermal Images of Direct Current Motor with Application of Area Perimeter Vector and Bayes Classifier

[1] Kennedy Space Center. (2012). Thermography Technique AT-9.Search in Google Scholar

[2] Tokarski, T., Wzorek, L., Dybiec, H. (2012). Microstructure and plasticity of hot deformed 5083 aluminum alloy produced by rapid solidification and hot extrusion. Archives of Metallurgy and Materials, 57 (4), 1253-1259.10.2478/v10172-012-0140-2Search in Google Scholar

[3] Krolczyk, G.M., Legutko, S. (2014). Experimental analysis by measurement of surface roughness variations in turning process of duplex stainless steel. Metrology and Measurement Systems, 21 (4), 759-770.10.2478/mms-2014-0060Search in Google Scholar

[4] Koscielny, J.M., Syfert, M. (2014). Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes. Bulletin of The Polish Academy of Sciences-Technical Sciences, 62 (3), 571-582.10.2478/bpasts-2014-0062Search in Google Scholar

[5] Umasankar, L., Kalaiarasi, N. (2014). Internal Fault Identification and Classification of Transformer with the Aid of Radial Basis Neural Network (RBNN). Arabian Journal for Science and Engineering, 39 (6), 4865-4873.10.1007/s13369-014-1030-xSearch in Google Scholar

[6] Glowacz, A. (2014). Diagnostics of synchronous motor based on analysis of acoustic signals with the use of line spectral frequencies and K-nearest neighbor classifier. Archives of Acoustics, 39 (2), 189-194.Search in Google Scholar

[7] Glowacz, A., Glowacz, W., Glowacz, Z. (2015). Recognition of armature current of DC generator depending on rotor speed using FFT, MSAF-1 and LDA. Eksploatacja i Niezawodnosc–Maintenance and Reliability, 17 (1), 64-69.10.17531/ein.2015.1.9Search in Google Scholar

[8] Pleban, D. (2014). Definition and measure of the sound quality of the machine. Archives of Acoustics, 39 (1), 17-23.Search in Google Scholar

[9] Sebok, M., Gutten, M., Kucera, M. (2011). Diagnostics of electric equipments by means of thermovision. Przeglad Elektrotechniczny, 87 (10), 313-317.Search in Google Scholar

[10] Glowacz, W. (2013). Diagnostics of induction motor based on spectral analysis of stator current with application of backpropagation neural network. Archives of Metallurgy and Materials, 58 (2), 559-562.10.2478/amm-2013-0037Search in Google Scholar

[11] Baranski, M., Decner, A., Polak, A. (2014). Selected diagnostic methods of electrical machines operating in industrial conditions. IEEE Transactions on Dielectrics and Electrical Insulation. 21 (5), 2047-2054.10.1109/TDEI.2014.004602Search in Google Scholar

[12] Zuber, N., Bajric, R., Sostakov, R. (2014). Gearbox faults identification using vibration signal analysis and artificial intelligence methods. Eksploatacja i Niezawodnosc–Maintenance and Reliability, 16 (1), 61-65.Search in Google Scholar

[13] Zhang, J.H., Ma, W.P., Lin, J.W., Ma, L., Jia, X.J. (2015). Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence. Measurement, 59, 73-87.10.1016/j.measurement.2014.09.045Search in Google Scholar

[14] Baranski, M. (2014). New vibration diagnostic method of PM generators and traction motors - detecting of vibrations caused by unbalance. In IEEE International Energy Conference (ENERGYCON), 13-16 May 2014. IEEE, 28-32.10.1109/ENERGYCON.2014.6850401Search in Google Scholar

[15] Swedrowski, L., Duzinkiewicz, K., Grochowski, M., Rutkowski, T. (2014). Use of neural networks in diagnostics of rolling-element bearing of the induction motor. Key Engineering Materials, 588, 333-342.10.4028/www.scientific.net/KEM.588.333Search in Google Scholar

[16] Lu, C., Tao, X.C., Zhang, W.J., Wang, Z.L. (2014). Machine integrated health models for condition-based maintenance. Tehnicki Vjesnik-Technical Gazette, 21 (6), 1377-1383.Search in Google Scholar

[17] Gornicka, D. (2014). Vibroacoustic symptom of the exhaust valve damage of the internal combustion engine. Journal of Vibroengineering, 16 (4), 1925-1933.Search in Google Scholar

[18] Glowacz, A., Glowacz, A., Korohoda, P. (2012). Recognition of color thermograms of synchronous motor with the application of image cross-section and linear perceptron classifier. Przeglad Elektrotechniczny, 88 (10A), 87-89.Search in Google Scholar

[19] Wegiel, T., Sulowicz, M., Borkowski, D. (2007). A distributed system of signal acquisition for induction motors diagnostic. In IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives, 6-8 September 2007. IEEE, 88-92.10.1109/DEMPED.2007.4393105Search in Google Scholar

[20] Rusinski, E., Moczko, P., Odyjas, P., Pietrusiak, D. (2014). Investigation of vibrations of a main centrifugal fan used in mine ventilation. Archives of Civil and Mechanical Engineering, 14 (4), 569-579.10.1016/j.acme.2014.04.003Search in Google Scholar

[21] Andonova, A.V., Hinov, N.L. (2014). Thermographic analysis of a bridge power converter. Journal of Electrical Engineering-Elektrotechnicky Casopis, 65 (6), 371-375.Search in Google Scholar

[22] Duspara, M., Sabo, K., Stoic, A. (2014). Acoustic emission as tool wear monitoring. Tehnicki Vjesnik-Technical Gazette, 21 (5), 1097-1101.Search in Google Scholar

[23] Zhao, Z., Wang, C., Zhang, Y.G., Sun, Y. (2014). Latest progress of fault detection and localization in complex electrical engineering. Journal of Electrical Engineering-Elektrotechnicky Casopis, 65 (1), 55-59.10.2478/jee-2014-0008Search in Google Scholar

[24] Abramov, I.V., Nikitin, Y.R., Abramov, A.I., Sosnovich, E.V., Bozek, P. (2014). Control and diagnostic model of brushless DC motor. Journal of Electrical Engineering-Elektrotechnicky Casopis. 65 (5), 277-282.10.2478/jee-2014-0044Search in Google Scholar

[25] Glowacz, A., Glowacz, A., Glowacz, Z. (2014). Recognition of monochrome thermal images of synchronous motor with the application of quadtree decomposition and backpropagation neural network. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 16 (1), 92–96.Search in Google Scholar

[26] Glowacz, A., Glowacz, A., Korohoda, P. (2014). Recognition of monochrome thermal images of synchronous motor with the application of binarization and nearest mean classifier. Archives of Metallurgy and Materials, 59 (1), 31-34.10.2478/amm-2014-0005Search in Google Scholar

[27] Glowacz, A., Glowacz, A., Glowacz, Z. (2015). Recognition of monochrome thermal images of synchronous motor with the application of skeletonization and classifier based on words. Archives of Metallurgy and Materials, 60 (1), 27-32.10.1515/amm-2015-0004Search in Google Scholar

[28] Stepien, K. (2014). Research on a surface texture analysis by digital signal processing methods. Tehnicki Vjesnik-Technical Gazette, 21 (3), 485-493.Search in Google Scholar

[29] Fidali, M., Urbanek, G. (2012). The application of evolutionary algorithms in the search of relevant statistical features of infrared images. Qirt Journal, 9 (1), 33-54.10.1080/17686733.2012.676905Search in Google Scholar

[30] Shapiro, L.G., Stockman, G.C. (2002). Computer Vision. Prentice Hall.Search in Google Scholar

[31] MathWorks. (2015). MATLAB and SimuLink for Technical Computing. www.mathworks.com.Search in Google Scholar

[32] Hachaj, T., Ogiela, M.R. (2013). Application of neural networks in detection of abnormal brain perfusion regions. Neurocomputing, 122 (Special Issue), 33-42.10.1016/j.neucom.2013.04.030Search in Google Scholar

[33] Augustyniak, P., Smolen, M., Mikrut, Z., Kantoch, E. (2014). Seamless tracing of human behavior using complementary wearable and house-embedded sensors. Sensors, 14 (5), 7831-7856.10.3390/s140507831406299724787640Search in Google Scholar

[34] Batko, W., Przysucha, B. (2014). Statistical analysis of the equivalent noise level. Archives of Acoustics, 39 (2), 195-198.Search in Google Scholar

[35] Dudek-Dyduch, E., Tadeusiewicz, R., Horzyk, A. (2009). Neural network adaptation process effectiveness dependent of constant training data availability. Neurocomputing, 72 (13-15), 3138-3149.10.1016/j.neucom.2009.03.017Search in Google Scholar

[36] Valis, D., Pietrucha-Urbanik, K. (2014). Utilization of diffusion processes and fuzzy logic for vulnerability assessment. Eksploatacja i Niezawodnosc–Maintenance and Reliability, 16 (1), 48-55.Search in Google Scholar

[37] Alshayeb, M., Eisa, Y., Ahmed MA. (2014). Object-Oriented Class Stability Prediction: A Comparison Between Artificial Neural Network and Support Vector Machine. Arabian Journal for Science and Engineering, 39 (11), 7865-7876.10.1007/s13369-014-1372-4Search in Google Scholar

[38] Mazurkiewicz, D. (2014). Computer-aided maintenance and reliability management systems for conveyor belts. Eksploatacja i Niezawodnosc–Maintenance and Reliability, 16 (3), 377-382.Search in Google Scholar

[39] Kundegorski, M., Jackson, P.J.B., Ziolko, B. (2014). Two-microphone dereverberation for automatic speech recognition of Polish. Archives of Acoustics, 39 (3), 411-420.Search in Google Scholar

[40] Murty, M.N., Devi, V.S. (2011). Bayes classifier. Pattern Recognition: An Algorithmic Approach. Springer, 86-102.Search in Google Scholar

[41] Krolczyk, G.M., Krolczyk, J.B., Legutko, S., Hunjet, A. (2014). Effect of the disc processing technology on the vibration level of the chipper during operations. Tehnicki Vjesnik-Technical Gazette, 21 (2), 447-450.Search in Google Scholar

[42] Jun, S., Kochan, O. (2014). Investigations of thermocouple drift irregularity impact on error of their inhomogeneity correction. Measurement Science Review, 14 (1), 29-34.10.2478/msr-2014-0005Search in Google Scholar

[43] Jaworek-Korjakowska, J., Tadeusiewicz, R. (2014). Determination of border irregularity in dermoscopic color images of pigmented skin lesions. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 26-30 August 2014. IEEE, 6459-6462.10.1109/EMBC.2014.694510725571475Search in Google Scholar

[44] Krolczyk, J.B. (2014), An attempt to predict quality changes in a ten-component granular system. Tehnicki Vjesnik-Technical Gazette, 21 (2), 255-261.Search in Google Scholar

[45] Dzwonkowski, A., Swedrowski, L. (2012). Uncertainty analysis of measuring system for instantaneous power research. Metrology and Measurement Systems, 19 (3), 573-582.10.2478/v10178-012-0050-7Search in Google Scholar