Recognition of acoustic signals of induction motor using fft, smofs-10 and ISVM

Glowacz, A.

doi:10.17531/ein.2015.4.12

Artykuł - szczegóły

Tytuł artykułu

Recognition of acoustic signals of induction motor using fft, smofs-10 and ISVM

Autorzy

Glowacz A.

Treść / Zawartość

Pełne teksty:

Pobierz

Pobierz

Identyfikatory

DOI

10.17531/ein.2015.4.12

Warianty tytułu

Rozpoznawanie sygnałów akustycznich silnika indukcyjnego z zastosowaniem fft, smofs-10 i ISVM

Języki publikacji

EN PL

Abstrakty

A correct diagnosis of electrical circuits is very essential in industrial plants. An article deals with a recognition method of early fault detection of induction motor. The described approach is based on patterns recognition. Acoustic signals of specific induction motor are analyzed patterns. Acoustic signals include information about motor state. The analysis of the patterns was conducted for three states of induction motor using Fast Fourier Transform (FFT), shortened method of frequencies selection (SMoFS-10) and Linear Support Vector Machine (LSVM). The results of calculations suggest that the method is efficient and can be also used for diagnostic purposes.

Prawidłowa diagnostyka obwodów elektrycznych jest bardzo istotna w zakładach przemysłowych. Artykuł zajmuje się metodą rozpoznawania stanów przedawaryjnych silnika indukcyjnego. Opisane podejście jest oparte na rozpoznawaniu wzorców. Sygnały akustyczne określonego silnika indukcyjnego są badanymi wzorcami. Sygnały akustyczne zawierają informację o stanie silnika. Analiza wzorców została przeprowadzona dla trzech stanów silnika indukcyjnego używając FFT, skróconej metody wyboru częstotliwości (SMoFS-10) i liniowej maszyny wektorów wspierających (LSVM). Wyniki obliczeń sugerują, że metoda jest skuteczna i może być również zastosowana dla celów diagnostycznych.

Słowa kluczowe

acoustic signal induction motor feature extraction classification

sygnał akustyczny silnik indukcyjny ekstrakcja cech klasyfikacja

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2015

Tom

Vol. 17, no. 4

Strony

569--574

Opis fizyczny

Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Glowacz A.

adglow@agh.edu.pl

AGH University of Science and Technology Faculty of Electrical Engineering, Automatics Computer Science and Biomedical Engineering Department of Automatics and Biomedical Engineering al. A. Mickiewicza 30, 30-059 Kraków, Poland

Bibliografia

1. Abramov IV, Nikitin YR, Abramov AI, Sosnovich EV, Bozek P. Control and Diagnostic Model of Brushless Dc Motor. Journal of Electrical Engineering-Elektrotechnicky Casopis 2014; 65 (5): 277-282, http://dx.doi.org/10.2478/jee-2014-0044.
2. Alshayeb M, Eisa Y, Ahmed MA. Object-Oriented Class Stability Prediction: A Comparison Between Artificial Neural Network and Support Vector Machine. Arabian Journal for Science and Engineering 2014; 39 (11): 7865-7876, http://dx.doi.org/10.1007/s13369-014-1372-4.
3. Andonova AV, Hinov NL. Thermographic Analysis of a Bridge Power Converter. Journal of Electrical Engineering-Elektrotechnicky Casopis 2014; 65 (6), 371-375.
4. Augustyniak P, Smolen M, Mikrut Z, Kantoch E. Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors. Sensors 2014; 14 (5): 7831-7856, http://dx.doi.org/10.3390/s140507831.
5. Baranski M. New vibration diagnostic method of PM generators and traction motors - detecting of vibrations caused by unbalance. 2014 IEEE International Energy Conference (ENERGYCON 2014); Book Series: IEEE International Energy Conference: 28-32.
6. Baranski M, Decner A, Polak A. Selected Diagnostic Methods of Electrical Machines Operating in Industrial Conditions. IEEE Transactions on Dielectrics and Electrical Insulation 2014; 21 (5): 2047-2054, http://dx.doi.org/10.1109/TDEI.2014.004602.
7. Dudek-Dyduch E, Tadeusiewicz R, Horzyk A. Neural network adaptation process effectiveness dependent of constant training data availability. Neurocomputing 2009; 72 (13-15): 3138-3149, http://dx.doi.org/10.1016/j.neucom.2009.03.017.
8. Duspara M, Sabo K, Stoic A. Acoustic emission as tool wear monitoring. Tehnicki Vjesnik-Technical Gazette 2014; 21 (5), 1097-1101.
9. Dzwonkowski A, Swedrowski L. Uncertainty analysis of measuring system for instantaneous power research. Metrology and Measurement Systems 2012; 19 (3): 573-582.
10. Glowacz A. 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 2014; 39 (2): 189-194.
11. Glowacz A. Diagnostics of DC and Induction Motors Based on the Analysis of Acoustic Signals. Measurement Science Review 2014; 14 (5): 257-262, http://dx.doi.org/10.2478/msr-2014-0035.
12. Glowacz A, Glowacz Z. Diagnostics of DC machine based on analysis of acoustic signals with application of MFCC and classifier based on words. Archives of Metallurgy and Materials 2012; 57 (1): 179-183, http://dx.doi.org/10.2478/v10172-012-0007-6.
13. Glowacz A, Glowacz Z. Diagnostics of induction motor based on analysis of acoustic signals with application of FFT and classifier based on words. Archives of Metallurgy and Materials 2010; 55 (3): 707-712.
14. Glowacz A, Glowacz A, Korohoda P. Recognition of Monochrome Thermal Images of Synchronous Motor with the Application of Binarization and Nearest Mean Classifier. Archives of Metallurgy and Materials 2014; 59 (1): 31-34, http://dx.doi.org/10.2478/amm-2014-0005.
15. Glowacz Z, Kozik J. Detection of synchronous motor inter-turn faults based on spectral analysis of park's vector. Archives of Metallurgy and Materials 2013; 58 (1): 19-23, http://dx.doi.org/10.2478/v10172-012-0144-y.
16. Hachaj T, Ogiela MR. Application of neural networks in detection of abnormal brain perfusion regions. Neurocomputing 2013; 122 (Special Issue): 33-42, http://dx.doi.org/10.1016/j.neucom.2013.04.030.
17. Jun S, Kochan O. Investigations of Thermocouple Drift Irregularity Impact on Error of their Inhomogeneity Correction. Measurement Science Review 2014; 14 (1): 29-34.
18. Krolczyk GM, Nieslony P, Legutko S. Determination of tool life and research wear during duplex stainless steel turning. Archives of Civil and Mechanical Engineering 2015; 15 (2): 347-354, http://dx.doi.org/10.1016/j.acme.2014.05.001.
19. Krolczyk GM, Krolczyk JB, Legutko S, Hunjet A. Effect of the disc processing technology on the vibration level of the chipper during operations. Tehnicki Vjesnik-Technical Gazette 2014; 21 (2), 447-450.
20. Krolczyk JB. An attempt to predict quality changes in a ten-component granular system. Tehnicki Vjesnik-Technical Gazette 2014; 21 (2), 255-261.
21. Kundegorski M, Jackson PJB, Ziolko B. Two-Microphone Dereverberation for Automatic Speech Recognition of Polish. Archives of Acoustics 2014; 39 (3): 411-420.
22. Kulka Z. Advances in Digitization of Microphones and Loudspeakers. Archives of Acoustics 2011; 36 (2): 419-436, http://dx.doi.org/10.2478/v10168-011-0030-z.
23. Mahmood A, Alsulaiman M, Muhammad G. Automatic Speaker Recognition Using Multi-Directional Local Features (MDLF). Arabian Journal for Science and Engineering 2014; 39 (5): 3799-3811, http://dx.doi.org/10.1007/s13369-014-1048-0.
24. MathWorks – MATLAB and SimuLink for Technical Computing 2015; www.mathworks.com.
25. Nadolny K, Kaplonek W. Analysis of Flatness Deviations for Austenitic Stainless Steel Workpieces after Efficient Surface Machining. Measurement Science Review 2014; 14 (4), 204-212, http://dx.doi.org/10.2478/msr-2014-0028.
26. Nafisi H, Abedi M, Gharehpetian GB. Locating Pd in Transformers through Detailed Model and Neural Networks. Journal of Electrical Engineering-Elektrotechnicky Casopis 2014; 65 (2), 75-82, http://dx.doi.org/10.2478/jee-2014-0011.
27. Paydarnia H, Hajiaghasi S, Abbaszadeh K. Improved Structure of PNN Using PCA in Transformer Fault Diagnostic. Arabian Journal for Science and Engineering 2014; 39 (6): 4845-4851, http://dx.doi.org/10.1007/s13369-014-1004-z.
28. Pleban D. Definition and Measure of the Sound Quality of the Machine. Archives of Acoustics 2014; 39 (1): 17-23.
29. Rusinski E, Moczko P, Odyjas P, Pietrusiak D. Investigation of vibrations of a main centrifugal fan used in mine ventilation. Archives of Civil and Mechanical Engineering 2014; 14 (4), 569-579, http://dx.doi.org/10.1016/j.acme.2014.04.003.
30. Stepien K. Research on a surface texture analysis by digital signal processing methods. Tehnicki Vjesnik-Technical Gazette 2014; 21 (3): 485-493.
31. Swedrowski L, Duzinkiewicz K, Grochowski M, Rutkowski T. Use of neural networks in diagnostics of rolling-element bearing of the induction motor. Smart Diagnostics V 2014; Book Series: Key Engineering Materials; 588: 333-342.
32. Turchenko I, Kochan V, Sachenko A, Kochan R, Stepanenko A, Daponte P, Grimaldi D. Simulation modeling of neural-based method of multi-sensor output signal recognition. 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings; Vols 1-5: 1530-1535, http://dx.doi.org/10.1109/IMTC.2006.328653.
33. Valis D, Zak L, Pokora O. Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data. Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability 2015; 229 (1): 36-45, http://dx.doi.org/10.1177/1748006X14547789.
34. Valis D, Pietrucha-Urbanik K. Utilization of diffusion processes and fuzzy logic for vulnerability assessment. Eksploatacja i Niezawodnosc Maintenance and Reliability 2014; 16 (1): 48-55.
35. Umasankar L, Kalaiarasi N. Internal Fault Identification and Classification of Transformer with the Aid of Radial Basis Neural Network (RBNN). Arabian Journal for Science and Engineering 2014; 39 (6): 4865-4873, http://dx.doi.org/10.1007/s13369-014-1030-x.
36. Wu RC, Tsai JI, Chiang CT, Ouyang CS. Detection of induction motor operation condition by acoustic signal. 8th IEEE International Conference on Industrial Informatics (INDIN) 2010: 792-797, http://dx.doi.org/10.1109/indin.2010.5549641.
37. Zhang DZ, Xia BK. Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor. Measurement Science Review 2014; 14 (4), 219-226, http://dx.doi.org/10.2478/msr-2014-0030.
38. Zhao Z, Wang C, Zhang YG, Sun Y. Latest progress of fault detection and localization in complex Electrical Engineering. Journal of Electrical Engineering-Elektrotechnicky Casopis 2014; 65 (1): 55-59, http://dx.doi.org/10.2478/jee-2014-0008.

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-da90e985-1f5f-49fb-968e-d3d3a23f9211