Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM

Open access

Abstract

This article discusses a system of recognition of acoustic signals of loaded synchronous motor. This software can recognize various types of incipient failures by means of analysis of the acoustic signals. Proposed approach uses the acoustic signals generated by loaded synchronous motor. A plan of study of the acoustic signals of loaded synchronous motor is proposed. Studies include following states: healthy loaded synchronous motor, loaded synchronous motor with shorted stator coil, loaded synchronous motor with shorted stator coil and broken coil, loaded synchronous motor with shorted stator coil and two broken coils. The methods such as FFT, method of selection of amplitudes of frequencies (MSAF-5), Linear Support Vector Machine were used to identify specific state of the motor. The proposed approach can keep high recognition rate and reduce the maintenance cost of synchronous motors.

1. 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.

2. 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.

3. Bicek M., Gotovac G., Miljavec D., Zupan S. (2015), Mechanical Failure Mode Causes of In-Wheel Motors, Strojniski vestnik – Journal of Mechanical Engineering, 61, 1, 74–85.

4. Cristianini N., Shawe-Taylor J. (2000), An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, First Edition, Cambridge: Cambridge University Press.

5. Czech P., Wojnar G., Burdzik R., Konieczny L., Warczek J. (2014), Application of the discrete wavelet transform and probabilistic neural networks in IC engine fault diagnostics, Journal of Vibroengineering, 16, 4, 1619–1639.

6. Czopek K. (2012), Cardiac Activity Based on Acoustic Signal Properties, Acta Physica Polonica A, 121, 1A, A42–A45.

7. 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.

8. Glowacz A. (2010), Diagnostics of dc machine based on sound recognition with application of LPC and GSDM, Przeglad Elektrotechniczny, 86, 6, 243–246.

9. Glowacz A. (2014), Diagnostics of DC and Induction Motors Based on the Analysis of Acoustic Signals, Measurement Science Review, 14, 5, 257–262.

10. Glowacz Z., Glowacz W. (2007), Mathematical model of DC motor for analysis of commutation processes, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Cracow, Poland, 461–464.

11. Glowacz A., Glowacz W. (2008), Sound recognition of dc machine with-application of FFT and backpropagation neural network, Przeglad Elektrotechniczny, 84, 9, 159–162.

12. Glowacz A., Glowacz A., Glowacz Z. (2012a), Diagnostics of Direct Current generator based on analysis of monochrome infrared images with the application of cross-sectional image and nearest neighbor classifier with Euclidean distance, Przeglad Elektrotechniczny, 88, 6, 154–157.

13. 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.

14. Glowacz A., Glowacz A., Korohoda P. (2012b), Recognition of Color Thermograms of Synchronous Motor with the Application of Image Cross-Section and Linear Perceptron Classifier, Przeglad Elektrotechniczny, 88, 10A, 87–89.

15. 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.

16. Glowacz Z., Kozik J. (2013), Detection of Synchronous Motor Inter-Turn Faults Based on Spectral Analysis of Park’S Vector, Archives of Metallurgy and Materials, 58, 1, 19–23.

17. Glowacz Z., Zdrojewski A. (2007), Diagnostics of commutator DC motor basing on spectral analysis of signals, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives, Cracow, Poland, 34–37.

18. Gornicka D. (2014), Vibroacoustic symptom of the exhaust valve damage of the internal combustion engine, Journal of Vibroengineering, 16, 4, 1925–1933.

19. Hachaj T., Ogiela M.R. (2013), Application of neural networks in detection of abnormal brain perfusion regions, Neurocomputing, 122 (Special Issue), 33–42.

20. Hachaj T., Ogiela M.R. (2011), CAD system for automatic analysis of CT perfusion maps, Opto-Electronics Review, 19, 1, 95–103.

21. Idziak P., Rawicki S. (2010), Analysis of stator deformations of a three-phase squirrel-cage induction motor, Przeglad Elektrotechniczny, 86, 4, 184–187.

22. Igras M., Ziolko B. (2014), The Role of Acoustic Features in Marking Accent and Delimiting Sentence Boundaries in Spoken Polish, Acta Physica Polonica A, 126, 6, 1246–1257.

23. Jakubiec J., Makowski P., Roj J. (2007), Neural reconstruction of nonlinear sensor input signal, 2007 IEEE Instrumentation & Measurement Technology Conference, Vols 1–5, Book Series: IEEE Instrumentation & Measurement Technology Conference, proceedings, 684–689.

24. Jaworek-Korjakowska J., Tadeusiewicz R. (2014), Determination of border irregularity in dermoscopic color images of pigmented skin lesions, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 2014, 6459–62, DOI:10.1109/EMBC.2014.6945107.

25. Jun S., Kochan O. (2014), Investigations of Thermocouple Drift Irregularity Impact on Error of their Inhomogeneity Correction, Measurement Science Review, 14, 1, 29–34.

26. Khan Z.F., Kannan A. (2014), Intelligent Segmentation of Medical Images using Fuzzy Bitplane Thresholding, Measurement Science Review, 14, 2, 94–101.

27. 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.

28. Krolczyk G., Raos P., Legutko S. (2014), Experimental analysis of surface roughness and surface texture of machined and fused deposition modelled parts, Tehnicki Vjesnik-Technical Gazette, 21, 1, 217–221.

29. Krolczyk J.B. (2014), An attempt to predict quality changes in a ten-component granular system, Tehnicki Vjesnik-Technical Gazette, 21, 2, 255–261.

30. Kudelcik J., Gutten M., Virdzek P. (2011), Measurement of electrical parameters of breakdown in transformer oil, Przeglad Elektrotechniczny, 87, 8, 159–162.

31. Li Z., Ma Z.Y., Liu Y.B., Teng W., Jiang R. (2015), Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network, Strojniski vestnik – Journal of Mechanical Engineering, 61, 1, 63–73.

32. MathWorks – MATLAB and SimuLink for Technical Computing 2014; www.mathworks.com.

33. Mazurkiewicz D. (2014), Computer-aided maintenance and reliability management systems for conveyor belts, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 16, 3, 377–382.

34. Nadolny K., Kaplonek W. (2014), Analysis of Flatness Deviations for Austenitic Stainless Steel Workpieces after Efficient Surface Machining, Measurement Science Review, 14, 4, 204–212.

35. Pleban D., Piochowicz J., Kosala K. (2013), The Inversion Method in Measuring Noise Emitted by Machines in Opencast Mines of Rock Material, International Journal of Occupational Safety and Ergonomics, 19, 2, 321–331.

36. Pribil J., Pribilova A., Frollo I. (2014), Mapping and Spectral Analysis of Acoustic Vibration in the Scanning Area of the Weak Field Magnetic Resonance Imager, Journal of Vibration and Acoustics-Transactions of the Asme, 136, 5, DOI: 10.1115/1.4027791.

37. 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.

38. Sebok M., Gutten M., Kucera M. (2011), Diagnostics of electric equipments by means of thermovision, Przeglad Elektrotechniczny, 87, 10, 313–317.

39. Smolnicki T., Stanco M., Pietrusiak D. (2013), Distribution of loads in the large size bearing – problems of identification. Tehnicki Vjesnik-Technical Gazette, 20, 5, 831–836.

40. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J. (2002), Least Squares Support Vector Machines, World Scientific, Singapore.

41. 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.

42. Turchenko I., Kochan V., Sachenko A., Kochan R., Stepanenko A., Daponte P., Grimaldi D. (2006), Simulation modeling of neural-based method of multi-sensor output signal recognition, IEEE Instrumentation and Measurement Technology Conference Proceedings, Vols. 1–5, Book Series: IEEE Instrumentation &Measurement Technology Conference, 1530–1535.

43. 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.

44. Valis D., Zak L., Walek A., Pietrucha-Urbanik K. (2014), Selected mathematical functions used for operation data information, Safety, Reliability and Risk Analysis: Beyond the Horizon, 22nd Annual Conference on European Safety and Reliability (ESREL), Amsterdam, Netherlands, 1303–1308.

45. Wu R.C., Tsai J.I., Chiang C.T., Ouyang C.S. (2010), Detection of induction motor operation condition by acoustic signal, 8th IEEE International Conference on Industrial Informatics (INDIN), 792–797.

46. 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.

47. Zuber N., Cvetkovic D., Bajric R. (2013), Multiple fault identification using vibration signal analysis and artificial intelligence methods, Acoustics & Vibration of Mechanical Structures, Book Series: Applied Mechanics and Materials, 430, 63–69.

Archives of Acoustics

The Journal of Institute of Fundamental Technological of Polish Academy of Sciences

Journal Information


IMPACT FACTOR 2016: 0.816
5-year IMPACT FACTOR: 0.835

CiteScore 2016: 1.15

SCImago Journal Rank (SJR) 2016: 0.432
Source Normalized Impact per Paper (SNIP) 2016: 0.948

Cited By

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 214 192 20
PDF Downloads 74 65 6