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

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

Open Access
|Jul 2015

References

  1. [1] Kennedy Space Center. (2012). Thermography Technique AT-9.
  2. [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/v10172-012-0140-2
  3. [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-2014-0060
  4. [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-2014-0062
  5. [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-x
  6. [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.
  7. [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.2015.1.9
  8. [8] Pleban, D. (2014). Definition and measure of the sound quality of the machine. Archives of Acoustics, 39 (1), 17-23.
  9. [9] Sebok, M., Gutten, M., Kucera, M. (2011). Diagnostics of electric equipments by means of thermovision. Przeglad Elektrotechniczny, 87 (10), 313-317.
  10. [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-2013-0037
  11. [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.2014.004602
  12. [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.
  13. [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.2014.09.045
  14. [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.2014.6850401
  15. [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.588.333
  16. [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.
  17. [17] Gornicka, D. (2014). Vibroacoustic symptom of the exhaust valve damage of the internal combustion engine. Journal of Vibroengineering, 16 (4), 1925-1933.
  18. [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.
  19. [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.2007.4393105
  20. [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.2014.04.003
  21. [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.
  22. [22] Duspara, M., Sabo, K., Stoic, A. (2014). Acoustic emission as tool wear monitoring. Tehnicki Vjesnik-Technical Gazette, 21 (5), 1097-1101.
  23. [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-2014-0008
  24. [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-2014-0044
  25. [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.
  26. [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-2014-0005
  27. [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-2015-0004
  28. [28] Stepien, K. (2014). Research on a surface texture analysis by digital signal processing methods. Tehnicki Vjesnik-Technical Gazette, 21 (3), 485-493.
  29. [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), 33-54.10.1080/17686733.2012.676905
  30. [30] Shapiro, L.G., Stockman, G.C. (2002). Computer Vision. Prentice Hall.
  31. [31] MathWorks. (2015). MATLAB and SimuLink for Technical Computing. www.mathworks.com.
  32. [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.2013.04.030
  33. [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/s140507831406299724787640
  34. [34] Batko, W., Przysucha, B. (2014). Statistical analysis of the equivalent noise level. Archives of Acoustics, 39 (2), 195-198.
  35. [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.2009.03.017
  36. [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.
  37. [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/s13369-014-1372-4
  38. [38] Mazurkiewicz, D. (2014). Computer-aided maintenance and reliability management systems for conveyor belts. Eksploatacja i Niezawodnosc–Maintenance and Reliability, 16 (3), 377-382.
  39. [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.
  40. [40] Murty, M.N., Devi, V.S. (2011). Bayes classifier. Pattern Recognition: An Algorithmic Approach. Springer, 86-102.
  41. [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.
  42. [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-2014-0005
  43. [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.2014.694510725571475
  44. [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.
  45. [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/v10178-012-0050-7
Language: English
Page range: 119 - 126
Submitted on: Dec 4, 2014
Accepted on: Jun 23, 2015
Published on: Jul 10, 2015
Published by: Slovak Academy of Sciences, Institute of Measurement Science
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2015 Adam Glowacz, Andrzej Glowacz, Zygfryd Glowacz, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.