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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

Measurement Science Review
Volume 15 (2015): Issue 3 (June 2015)
By: ,   and    
Open Access
|Jul 2015
References

References

  1. [1] Kennedy Space Center. (2012)..
    Search in Google Scholar
  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., 57 (4), 1253-1259.
    Open DOISearch in Google Scholar
  3. [3] Krolczyk, G.M., Legutko, S. (2014). Experimental analysis by measurement of surface roughness variations in turning process of duplex stainless steel., 21 (4), 759-770.
    Open DOISearch in Google Scholar
  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., 62 (3), 571-582.
    Open DOISearch in Google Scholar
  5. [5] Umasankar, L., Kalaiarasi, N. (2014). Internal Fault Identification and Classification of Transformer with the Aid of Radial Basis Neural Network (RBNN)., 39 (6), 4865-4873.
    Open DOISearch in Google Scholar
  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., 39 (2), 189-194.
    Search in Google Scholar
  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., 17 (1), 64-69.
    Open DOISearch in Google Scholar
  8. [8] Pleban, D. (2014). Definition and measure of the sound quality of the machine., 39 (1), 17-23.
    Search in Google Scholar
  9. [9] Sebok, M., Gutten, M., Kucera, M. (2011). Diagnostics of electric equipments by means of thermovision., 87 (10), 313-317.
    Search in Google Scholar
  10. [10] Glowacz, W. (2013). Diagnostics of induction motor based on spectral analysis of stator current with application of backpropagation neural network., 58 (2), 559-562.
    Open DOISearch in Google Scholar
  11. [11] Baranski, M., Decner, A., Polak, A. (2014). Selected diagnostic methods of electrical machines operating in industrial conditions.. 21 (5), 2047-2054.
    Open DOISearch in Google Scholar
  12. [12] Zuber, N., Bajric, R., Sostakov, R. (2014). Gearbox faults identification using vibration signal analysis and artificial intelligence methods., 16 (1), 61-65.
    Search in Google Scholar
  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., 59, 73-87.
    Open DOISearch in Google Scholar
  14. [14] Baranski, M. (2014). New vibration diagnostic method of PM generators and traction motors - detecting of vibrations caused by unbalance. In, 13-16 May 2014. IEEE, 28-32.
    Open DOISearch in Google Scholar
  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., 588, 333-342.
    Open DOISearch in Google Scholar
  16. [16] Lu, C., Tao, X.C., Zhang, W.J., Wang, Z.L. (2014). Machine integrated health models for condition-based maintenance., 21 (6), 1377-1383.
    Search in Google Scholar
  17. [17] Gornicka, D. (2014). Vibroacoustic symptom of the exhaust valve damage of the internal combustion engine., 16 (4), 1925-1933.
    Search in Google Scholar
  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., 88 (10A), 87-89.
    Search in Google Scholar
  19. [19] Wegiel, T., Sulowicz, M., Borkowski, D. (2007). A distributed system of signal acquisition for induction motors diagnostic. In, 6-8 September 2007. IEEE, 88-92.
    Open DOISearch in Google Scholar
  20. [20] Rusinski, E., Moczko, P., Odyjas, P., Pietrusiak, D. (2014). Investigation of vibrations of a main centrifugal fan used in mine ventilation., 14 (4), 569-579.
    Open DOISearch in Google Scholar
  21. [21] Andonova, A.V., Hinov, N.L. (2014). Thermographic analysis of a bridge power converter., 65 (6), 371-375.
    Search in Google Scholar
  22. [22] Duspara, M., Sabo, K., Stoic, A. (2014). Acoustic emission as tool wear monitoring., 21 (5), 1097-1101.
    Search in Google Scholar
  23. [23] Zhao, Z., Wang, C., Zhang, Y.G., Sun, Y. (2014). Latest progress of fault detection and localization in complex electrical engineering., 65 (1), 55-59.
    Open DOISearch in Google Scholar
  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.65 (5), 277-282.
    Open DOISearch in Google Scholar
  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., 16 (1), 92–96.
    Search in Google Scholar
  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., 59 (1), 31-34.
    Open DOISearch in Google Scholar
  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., 60 (1), 27-32.
    Open DOISearch in Google Scholar
  28. [28] Stepien, K. (2014). Research on a surface texture analysis by digital signal processing methods., 21 (3), 485-493.
    Search in Google Scholar
  29. [29] Fidali, M., Urbanek, G. (2012). The application of evolutionary algorithms in the search of relevant statistical features of infrared images., 9 (1), 33-54.
    Open DOISearch in Google Scholar
  30. [30] Shapiro, L.G., Stockman, G.C. (2002).. Prentice Hall.
    Search in Google Scholar
  31. [31] MathWorks. (2015)...
    Search in Google Scholar
  32. [32] Hachaj, T., Ogiela, M.R. (2013). Application of neural networks in detection of abnormal brain perfusion regions., 122 (Special Issue), 33-42.
    Open DOISearch in Google Scholar
  33. [33] Augustyniak, P., Smolen, M., Mikrut, Z., Kantoch, E. (2014). Seamless tracing of human behavior using complementary wearable and house-embedded sensors., 14 (5), 7831-7856.
    Open DOISearch in Google Scholar
  34. [34] Batko, W., Przysucha, B. (2014). Statistical analysis of the equivalent noise level., 39 (2), 195-198.
    Search in Google Scholar
  35. [35] Dudek-Dyduch, E., Tadeusiewicz, R., Horzyk, A. (2009). Neural network adaptation process effectiveness dependent of constant training data availability., 72 (13-15), 3138-3149.
    Open DOISearch in Google Scholar
  36. [36] Valis, D., Pietrucha-Urbanik, K. (2014). Utilization of diffusion processes and fuzzy logic for vulnerability assessment., 16 (1), 48-55.
    Search in Google Scholar
  37. [37] Alshayeb, M., Eisa, Y., Ahmed MA. (2014). Object-Oriented Class Stability Prediction: A Comparison Between Artificial Neural Network and Support Vector Machine.39 (11), 7865-7876.
    Open DOISearch in Google Scholar
  38. [38] Mazurkiewicz, D. (2014). Computer-aided maintenance and reliability management systems for conveyor belts., 16 (3), 377-382.
    Search in Google Scholar
  39. [39] Kundegorski, M., Jackson, P.J.B., Ziolko, B. (2014). Two-microphone dereverberation for automatic speech recognition of Polish., 39 (3), 411-420.
    Search in Google Scholar
  40. [40] Murty, M.N., Devi, V.S. (2011). Bayes classifier.Springer, 86-102.
    Search in Google Scholar
  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., 21 (2), 447-450.
    Search in Google Scholar
  42. [42] Jun, S., Kochan, O. (2014). Investigations of thermocouple drift irregularity impact on error of their inhomogeneity correction., 14 (1), 29-34.
    Open DOISearch in Google Scholar
  43. [43] Jaworek-Korjakowska, J., Tadeusiewicz, R. (2014). Determination of border irregularity in dermoscopic color images of pigmented skin lesions. In26-30 August 2014. IEEE, 6459-6462.
    Open DOISearch in Google Scholar
  44. [44] Krolczyk, J.B. (2014), An attempt to predict quality changes in a ten-component granular system., 21 (2), 255-261.
    Search in Google Scholar
  45. [45] Dzwonkowski, A., Swedrowski, L. (2012). Uncertainty analysis of measuring system for instantaneous power research., 19 (3), 573-582.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.1515/msr-2015-0018 | Journal eISSN: 1335-8871
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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
Keywords:
Thermal image,
fault detection,
identification methods,
pattern recognition
Related subjects:
Engineering,
Electrical engineering,
Control engineering, metrology and testing

© 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.

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