Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

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An early fault diagnostic method of Direct Current motors was presented in this article. The proposed method used acoustic signals of a motor. A method of feature extraction called MSAF-RATIO30-EXPANDED (method of selection of amplitudes of frequencies - ratio 30% of maximum of amplitude - expanded) was presented and implemented. An analysis of proposed method was carried out for early fault states of a real DC motor. Four following states of the DC motor were measured and analyzed: the healthy DC motor, DC motor with 3 shorted rotor coils, DC motor with 6 shorted rotor coils, DC motor with a broken coil. Measured states were caused by natural degradation of the DC motor. The obtained results of analysis were good. The presented early fault diagnostic method can be used for protection of DC motors.

[1] Glowacz W., Glowacz Z., Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier, Archives of Electrical Engineering, vol. 64, no. 1, pp. 29-35 (2015).

[2] Glowacz A., Glowacz Z., Diagnostics of stator faults of the single-phase induction motor using thermal images, MoASoS and selected classifiers, Measurement, vol. 93, pp. 86-93 (2016).

[3] Glowacz A., Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM, Archives of Acoustics, vol. 40, no. 2, pp. 197-203 (2015).

[4] Gonzalez-Cordoba J.L., Granados-Lieberman D., Osornio-Rios R.A. et al., Methodology for overheating identification on induction motors under voltage unbalance conditions in industrial processes, Journal of Scientific & Industrial Research, vol. 75, no. 2, pp. 100-107 (2016).

[5] Wegiel T., Sulowicz M., Borkowski D., A distributed system of signal acquisition for induction motors diagnostic, IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives, Cracow, POLAND, pp. 261-265 (2007).

[6] Irfan M., Saad N., Ibrahim R., Asirvadam V.S., An on-line condition monitoring system for induction motors via instantaneous power analysis, Journal of Mechanical Science and Technology, vol. 29, no. 4, pp. 1483-1492 (2015).

[7] Krolczyk G.M., Krolczyk J.B., Legutko S., Hunjet A., Effect of the disc processing technology on the vibration level of the chipper during operations, Tehnicki Vjesnik-Technical Gazette, vol. 21, no. 2, pp. 447-450 (2014).

[8] Carletti E., Miccoli G., Pedrielli F., Parise G., Vibroacoustic measurements and simulations applied to external gear pumps. An integrated simplified approach, Archives of Acoustics, vol. 41, no. 2, pp. 285-296 (2016).

[9] Li Z.X., Jiang Y., Hu C., Peng Z., Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review, Measurement, vol. 90, pp. 4-19 (2016).

[10] Perun G., Stanik Z., Evaluation of state of rolling bearings mounted in vehicles with use of vibration signals, Archives of Metallurgy and Materials, vol. 60, no. 3, pp. 1679-1683 (2015).

[11] Jozwik J., Mika D., Diagnostics of workpiece surface condition based on cutting tool vibrations during machining, Advances in Science and Technology Research Journal, vol. 9, no. 26, pp. 57-65 (2015).

[12] Lara R., Jimenez-Romero R., Perez-Hidalgo F., Redel-Macias M.D., Influence of constructive parameters and power signals on sound quality and airborne noise radiated by inverter-fed induction motors, Measurement, vol. 73, pp. 503-514 (2015).

[13] Caesarendra W., Kosasih B., Tieu A.K. et al., Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing, Mechanical Systems and Signal Processing, vol. 72-73, pp. 134-159 (2016).

[14] Figlus T., Liscak S., Wilk A., Aazarz B., Condition monitoring of engine timing system by using wavelet packet decomposition of a acoustic signal, Journal of Mechanical Science and Technology, vol. 28, no. 5, pp. 1663-1671 (2014).

[15] Zhang X., Feng N.Z., Wang Y., Shen Y., Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy, Journal of Sound and Vibration, vol. 339, pp. 419-432 (2015).

[16] Pleban D., Definition and measure of the sound quality of the machine, Archives of Acoustics. vol. 39, no. 1, pp. 17-23 (2014).

[17] Jena D.P., Panigrahi S.N., Automatic gear and bearing fault localization using vibration and acoustic signals, Applied Acoustics, vol. 98, pp. 20-33 (2015).

[18] Jozwik J., Identification and monitoring of noise sources of CNC machine tools by acoustic Holography methods, Advances in Science and Technology-Research Journal, vol. 10, no. 30, pp. 127-137 (2016).

[19] Bedkowski B., Madej J., The innovative design concept of thermal model for the calculation of the electromagnetic circuit of rotating electrical machines, Eksploatacja i Niezawodnosc - Maintenance and Reliability, vol. 17, no. 4, pp. 481-486 (2015).

[20] Glowacz A., Glowacz A., Glowacz Z., Recognition of thermal images of direct current motor with application of area perimeter vector and Bayes classifier, Measurement Science Review, vol. 15, no. 3, pp. 119-126 (2015).

[21] Sebok M., Gutten M., Kucera M., Diagnostics of electric equipments by means of thermovision, Przeglad Elektrotechniczny, vol. 87, no. 10, pp. 313-317 (2011).

[22] Smalcerz A., Aspects of application of industrial robots in metallurgical processes, Archives of Metallurgy and Materials, vol. 58, no. 1, pp. 203-209 (2013).

[23] Krolczyk J. B., Gapinski B., Krolczyk G. M., Samardzic I., Maruda R. W., Soucek K., Legutko S., Nieslony P., Javadi Y., Stas L., Topographic inspection as a method of weld joint diagnostic, Thenicki Vjesnik-Technical Gazette, vol. 23, no. 1, pp. 301-306 (2016).

[24] Demenko A., Description of electrical machine windings in the finite element space, COMPELThe international journal for computation and mathematics in electrical and electronic engineering, vol. 27, no. 4, pp. 711-719 (2008).

[25] 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, vol. 55, no. 3, pp. 707-712 (2010).

[26] Stepien K., Research on a surface texture analysis by digital signal processing methods. Tehnicki Vjesnik-Technical Gazette, vol. 21, no. 3, pp. 485-493 (2014).

[27] Duspara M., Sabo K., Stoic A., Acoustic emission as tool wear monitoring, Tehnicki Vjesnik-Technical Gazette, vol. 21, no. 5, pp. 1097-1101 (2014).

[28] Michalak M., Sikora M., Sobczyk J., Analysis of the longwall conveyor chain based on a harmonic analysis, Eksploatacja i Niezawodnosc - Maintenance and Reliability, vol. 15, no. 4, pp. 332-336 (2013).

[29] Valis D., Pietrucha-Urbanik K., Utilization of diffusion processes and fuzzy logic for vulnerability assessment, Eksploatacja i Niezawodnosc - Maintenance and Reliability, vol. 16, no. 1, pp. 48-55 (2014).

[30] Gorny Z., Kluska-Nawarecka S., Wilk-Kolodziejczyk D., Regulski K., Methodology for the construction of a rule-based knowledge base enabling the selection of appropriate bronze heat treatment parameters using rough sets, Archives of Metallurgy and Materials, vol. 60, no. 1, pp. 309-312 (2015).

[31] Kundegorski M., Jackson P.J.B., Ziolko B., Two-Microphone dereverberation for automatic speech recognition of Polish, Archives of Acoustics, vol. 39, no. 3, pp. 411-420 (2014).

[32] Valis D., Zak L., Pokora O., System condition estimation based on selected tribodiagnostic data, Quality and Reliability Engineering International, vol. 32, no. 2, pp. 635-645 (2016).

[33] Hachaj T., Pattern classification methods for analysis and visualization of brain perfusion CT maps, Computational Intelligence Paradigms in Advanced Pattern Classification, Book Series: Studies in Computational Intelligence, vol. 386, pp. 145-170 (2012).

[34] Jaworek-Korjakowska J., Kleczek P., Automatic classification of specific melanocytic lesions using artificial intelligence, BioMed Research International. Article Number: 8934242 (2016).

[35] Deptula A., Kunderman D., Osinski P., Radziwanowska U., Wlostowski R., Acoustic diagnostics applications in the study of technical condition of combustion engine, Archives of Acoustics, vol. 41, no. 2, pp. 345-350 (2016).

[36] Orzechowski P., Boryczko K., Parallel approach for visual clustering of protein databases, Computing and Informatics, vol. 29, no. 6, pp. 1221-1231 (2010).

[37] Jun S., Kochan O., Investigations of thermocouple drift irregularity impact on error of their inhomogeneity correction, Measurement Science Review, vol. 14, no. 1, pp. 29-34 (2014).

[38] Roj J., Cichy A., Method of measurement of capacitance and dielectric loss factor using artificial neural networks, Measurement Science Review, vol. 15, no. 3, pp. 127-131 (2015).

[39] Zhang Y.G., Yang J.Y., Wang K.C., Wang Z.P., Wind power prediction considering nonlinear atmospheric disturbances, Energies, vol. 8, no. 1, pp. 475-489 (2015).

[40] Jamroz D., Niedoba T., Application of multidimensional data visualization by means of self-organizing Kohonen maps to evaluate classification possibilities of various coal types, Archives of Mining Sciences, vol. 60, no. 1, pp. 39-50 (2015).

[41] Jun S., Kochan O., Kochan V., Wang CZ., Development and investigation of the method for compensating thermoelectric inhomogeneity error, International Journal of Thermophysics, vol. 37, no. 1, (2016).

[42] Panek D., Skalski A., Gajda J., Tadeusiewicz R., Acoustic analys is assessment in speech pathology detection, International Journal of Applied Mathematics and Computer Science, vol. 25, no. 3, pp. 631-643 (2015).

[43] Marzec M., Koprowski R., Wrobel Z., Methods of face localization in thermograms, Biocybernetics and Biomedical Engineering, vol. 35, no. 2, pp. 138-146 (2015).

[44] Jiang Y., Li Z.X., Zhang C., Hu C., Peng Z., On the bi-dimensional variational decomposition applied to nonstationary vibration signals for rolling bearing crack detection in coal cutters, Measurement Science and Technology, vol. 27, no. 6, Article Number: 065103 (2016).

[45] Hwang D.H., Youn Y.W., Sun J.H et al., Support vector machine based bearing fault diagnosis for induction motors using vibration signals, Journal of Electrical Engineering & Technology, vol. 10, no. 4, pp. 1558-1565 (2015).

Archives of Electrical Engineering

The Journal of Polish Academy of Sciences

Journal Information

CiteScore 2016: 0.71

SCImago Journal Rank (SJR) 2016: 0.238
Source Normalized Impact per Paper (SNIP) 2016: 0.535

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