DC Motor Fault Analysis with the Use of Acoustic Signals, Coiflet Wavelet Transform, and K-Nearest Neighbor Classifier

• Autorzy: Glowacz A.

Identyfikatory

DOI

10.1515/aoa-2015-0035

• Języki publikacji: EN

Abstrakty

EN

This paper focuses on testing the monitoring system of the Direct Current motor. This system gives the possibility of diagnosing various types of failures by means of analysis of acoustic signals. The applied method is based on a study of acoustic signals generated by the DC motor. A study plan of the DC motor’s acoustic signal was proposed. Studies were conducted for a faultless DC motor and Direct Current motor with 3 shorted rotor coils. Coiflet wavelet transform and K-Nnearest neighbor classifier with Euclidean distance were used to identify the incipient fault. This approach keeps the motor operating in acceptable condition for a long time and is also inexpensive.

Słowa kluczowe

EN

acoustic signal; Coiflet wavelet; fault detection; DC motor

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2015
• Tom: Vol. 40, No. 3
• Strony: 321--327
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr., fot.

Twórcy

autor

Glowacz A.

• AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland

Bibliografia

• 1. Abdesh Shafiel Kafiey Khan M., Azizzur Rahman M. (2010), Wavelet Based Diagnosis and Protection of Electric Motors, Fault Detection, Chapter 11, 512 pages, Publisher: InTech, Chapters DOI: 10.5772/9068.
• 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. 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.
• 4. Czopek K. (2012), Cardiac Activity Based on Acoustic Signal Properties, Acta Physica Polonica A, 121, 1A, A42–A45.
• 5. 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.
• 6. Duspara M., Sabo K., Stoic A. (2014), Acoustic emission as tool wear monitoring, Tehnicki Vjesnik-Technical Gazette, 21, 5, 1097–1101.
• 7. Dzwonkowski A., Swedrowski L. (2012), Uncertainty analysis of measuring system for instantaneous power research, Metrology and Measurement Systems, 19, 3, 573–582.
• 8. 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.
• 9. 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. Glowacz A., Glowacz A., Glowacz Z. (2012), 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.
• 11. Glowacz A. (2014), Diagnostics of DC and Induction Motors Based on the Analysis of Acoustic Signals, Measurement Science Review, 14, 5, 257–262.
• 12. Gornicka D. (2014), Vibroacoustic symptom of the exhaust valve damage of the internal combustion engine, Journal of Vibroengineering, 16, 4, 1925–1933.
• 13. Gutten M., Jurcik J., Brandt M., Polansky R. (2011), Mechanical effects of short-circuit currents analysis on autotransformer windings, Przeglad Elektrotechniczny, 87, 7, 272–275.
• 14. Gutten M., Trunkvalter M. (2010), Thermal effects of short-circuit current on winding in transformer oil, Przeglad Elektrotechniczny, 86, 3, 242–246.
• 15. Hachaj T., Ogiela M.R. (2013), Application of neural networks in detection of abnormal brain perfusion regions, Neurocomputing, 122 (Special Issue), 33–42.
• 16. Huang S.J., Hsieh C.T. (2002), Coiflet wavelet transform applied to inspect power system disturbancegenerated signals, IEEE Transactions on Aerospace and Electronic Systems, 38, 1, 204–210.
• 17. Igras M., Ziolko B. (2013), Wavelet method for breath detection in audio signals, IEEE International Conference on Multimedia and Expo (ICME 2013), San Jose, CA, Jul 15–19.
• 18. Jawadekar A.U., Dhole G.M., Paraskar S.R. (2012), Signal Processing based Wavelet Approach for Fault Detection of Induction Motor, International Journal of Science, Spirituality, Business and Technology, 1, 1, 70–75.
• 19. 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, Book Series: IEEE Engineering in Medicine and Biology Society Conference Proceedings, 6459–6462, DOI:10.1109/EMBC.2014.6945107.
• 20. Jun S., Kochan O. (2014), Investigations of Thermocouple Drift Irregularity Impact on Error of their Inhomogeneity Correction, Measurement Science Review, 14, 1, 29–34.
• 21. Krolczyk G.M., Krolczyk J.B., Legutko S., Hunjet A. (2014a), Effect of the disc processing technology on the vibration level of the chipper during operations, Tehnicki Vjesnik-Technical Gazette, 21, 2, 447–450.
• 22. Krolczyk G., Legutko S., Nieslony P., Gajek M. (2014b), Study of the surface integrity microhardness of austenitic stainless steel after turning, Tehnicki Vjesnik-Technical Gazette, 21, 6, 1307–1311.
• 23. Krolczyk J.B. (2014), An attempt to predict quality changes in a ten-component granular system, Tehnicki Vjesnik-Technical Gazette, 21, 2, 255–261.
• 24. Kulka Z. (2011), Advances in Digitization of Microphones and Loudspeakers, Archives of Acoustics, 36, 2, 419–436. HK., Xu FJ., Liu HY., Zhang XF. (2015), Incipient fault information determination for rolling element bearing based on synchronous averaging reassigned wavelet scalogram, Measurement, 65, 1–10, DOI: 10.1016/j.measurement.2014.12.032.
• 25. Li HK., Xu FJ., Liu HY., Zhang XF. (2015), Incipient fault information determination for rolling element bearing based on synchronous averaging reassigned wavelet scalogram, Measurement, 65, 1–10, DOI: 10.1016/j.measurement.2014.12.032.
• 26. MathWorks – MATLAB and SimuLink for Technical Computing 2014; www.mathworks.com.
• 27. Mazurkiewicz D. (2014), Computer-aided maintenance and reliability management systems for conveyor belts, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 16, 3, 377–382.
• 28. Nawarecki E., Kluska-Nawarecka S., Regulski K. (2012), Multi-aspect Character of the Man-Computer Relationship in a Diagnostic-Advisory System, Human-computer systems interaction: Backgrounds and applications 2. Pt 1, Book Series: Advances in Intelligent and Soft Computing, 98, 85–102.
• 29. Niklewicz M., Smalcerz A. (2010), Application of three-coil cylindrical inductor in induction heating of gears, Przeglad Elektrotechniczny, 86, 5, 333–335.
• 30. Pleban D. (2014), Definition and Measure of the Sound Quality of the Machine, Archives of Acoustics, 39, 1, 17–23.
• 31. Pribil J., Pribilova A., Durackova D. (2014), Evaluation of Spectral and Prosodic Features of Speech Affected by Orthodontic Appliances Using the GMM Classifier, Journal of Electrical Engineering-Elektrotechnicky Casopis, 65, 1, 30–36.
• 32. Regulski K., Szeliga D., Kusiak J. (2014), Data exploration approach versus sensitivity analysis for optimization of metal forming processes, Material Forming Esaform 2014, Book Series: Key Engineering Materials, 611–612, 1390–1395.
• 33. Roj J. (2013), Neural Network Based Real-time Correction of Transducer Dynamic Errors, Measurement Science Review, 13, 6, 286–291.
• 34. 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.
• 35. Stepien K. (2014), Research on a surface texture analysis by digital signal processing methods, Tehnicki Vjesnik-Technical Gazette, 21, 3, 485–493.
• 36. Stepien K., Makiela W. (2013), An analysis of deviations of cylindrical surfaces with the use of wavelet transform, Metrology and Measurement Systems, 20, 1, 139–150.
• 37. Stepien K., Makiela W., Stoic A., Samardzic I. (2015), Defining the criteria to select the wavelet type for the assessment of surface quality, Tehnicki Vjesnik-Technical Gazette, 22, 3, 781–784.
• 38. 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.
• 39. 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.
• 40. Valis D., Zak L., Pokora O. (2015), 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, 229, 1, 36–45.
• 41. Wegiel T., Sulowicz M., Borkowski D. (2007), A distributed system of signal acquisition for induction motors diagnostic, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives, Cracow, Poland, 88–92.
• 42. Zhang DZ., Xia BK. (2014), Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor, Measurement Science Review, 14, 4, 219–226.
• 43. Ziolko M., Galka J., Ziolko B., Drwiega T. (2010), Perceptual Wavelet Decomposition for Speech Segmentation, 11th Annual Conference of the International Speech Communication Association 2010 (INTERSPEECH 2010), Vols. 3 and 4, 2234–2237.