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Diagnostics of synchronous motor based on analysis of acoustic signals with application of MFCC and Nearest Mean classifier

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Języki publikacji
EN
Abstrakty
EN
The paper presents method of diagnostics of imminent failure conditions of synchronous motor. This method is based on a study of acoustic signals generated by synchronous motor. Sound recognition system is based on algorithms of data processing, such as MFCC and Nearest Mean classifier with cosine distance. Software to recognize the sounds of synchronous motor was implemented. The studies were carried out for four imminent failure conditions of synchronous motor. The results confirm that the system can be useful for detecting damage and protect the engines.
Rocznik
Strony
183--188
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
  • University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków
autor
  • University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków
autor
  • University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków
Bibliografia
  • [1] Pietrowski S., Pisarek B., Gumienny, G., Computer-aided control of high-quality cast iron, Archives of Foundry Engineering, 2008, Vol. 8, iss. 1, pp. 101-108.
  • [2] Stawarz M., Quality control of cast brake discs, Archives of Foundry Engineering, 2008, Vol. 8, iss. 1, pp. 119-122.
  • [3] Macioł A., Stawowy A., Wrona R., Shell expert system for technological knowledge acquisition and access, Archives of Foundry Engineering, 2008 vol. 8 iss. 3 pp. 75–80.
  • [4] Wrona R., Stawowy A., Macioł A., Methodological aspects of systemic designing of foundry plants, Archives of Foundry Engineering, 2008 Vol. 8 iss. 3 pp. 125–128.
  • [5] Dańko J., Zych J., Dańko R., Diagnostic methods of technological properties and casting cores quality, Archives of Metallurgy and Materials, 2009 Vol. 54 iss. 2 pp. 381–392.
  • [6] Głowacz Z., Zdrojewski A., Diagnostics of commutator DC motor using spectral analysis method, Przegląd Elektrotechniczny, 85 (2009) nr 1, pp. 147-150.
  • [7] Sałat R., Osowski S., Siwek K., Principal Component Analysis for feature selection at the diagnosis of electrical circuits, Przegląd Elektrotechniczny, 2003, No 10, pp. 667-670.
  • [8] Głowacz A., Głowacz W., Sound recognition of dc machine with application of FFT and back propagation neural network, Przegląd Elektrotechniczny (Electrical Review), R. 84, NR 9/2008, pp.159-162.
  • [9] Lee K., Effective Approaches to Extract Features and Classify Echoes in Long Ultrasound Signals from Metal Shafts, Ph. D. dissertation, Brisbane, Australia, 2006.
  • [10] The MARF Development Group, Modular Audio Recognition Framework v.0.3.0-devel-20050606 and its Applications, Application note, Montreal, Quebec, Canada, 2005.
  • [11] Ganchev T., Fakotakis N., Kokkinakis G., Comparative evaluation of various MFCC implementations on the speaker verification task. Proceedings of 10th International Conference on Speech and Computer (SPECOM 2005), Vol. 1, pp. 191–194.
  • [12] Slaney M. "Auditory Toolbox. Version 2", Technical Report #1998-010, Interval Research Corporation, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f6ff9391-6662-4a51-aa73-e5aeb85f027f
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