Archives of Acoustics, 44, 2, pp. 267–276, 2019

Application of Acoustic Signals for Rectifier Fault Detection in Brushless Synchronous Generator

Iran University of Science and Technology
Iran, Islamic Republic of

Abolfazl VAHEDI
Iran University of Science and Technology
Iran, Islamic Republic of

One of the most important issues that power companies face when trying to reduce time and cost maintenance is condition monitoring. In electricity market worldwide, a significant amount of electrical energy is produced by synchronous machines. One type of these machines is brushless synchronous generators in which the rectifier bridge is mounted on rotating shafts. Since bridge terminals are not accessible in this type of generators, it is difficult to detect the possible faults on the rectifier bridge. Therefore, in this paper, a method is proposed to facilitate the rectifier fault detection. The proposed method is then evaluated by applying two conventional kinds of faults on rectifier bridges including one diode open-circuit and two diode open-circuit (one phase open-circuit of the armature winding in the auxiliary generator in experimental set). To extract suitable features for fault detection, the wavelet transform has been used on recorded audio signals. For classifying faulty and healthy states, K-Nearest Neighbours (KNN) supervised classification method was used. The results show a good accuracy of the proposed method.
Keywords: acoustic emission; wavelet transform; acoustic emission; wavelet transform; K-Nearest Neighbours; fault detection; brushless generator
Full Text: PDF


Akin M. (2002), Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals, Journal of Medical Systems, 26, 3, 241–247, doi: 10.1023/A:1015075101937.

Babu Thamba N. et al. (2018), Application of EMD, ANN and DNN for self-aligning bearing fault diagnosis, Archives of Acoustics, 43, 2, 163–175, doi: 10.24425/aoa.2018.125166.

Cover T., Hart P. (1967), Nearest neighbor pattern classification, IEEE Transactions on Information Theory, 13, 1, 21–27, doi: 10.1109/TIT.1967.1053964.

Chacon J.L.F., Kappatos V., Balachandran W., Gan T.H. (2015)., A novel approach for incipient defect detection in rolling bearings using acoustic emission technique, Applied Acoustics, 89, 88–100, doi: 10.1016/j.apacoust.2014.09.002.

Glowacz A. (2015), Recognition of acoustic signals of loaded synchronous motor using FFT, MSAF-5 and LSVM, Archives of Acoustics, 40, 2, 197–203, doi: 10.1515/aoa-2015-0022.

Glowacz A. (2016), Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers, Tehnicki vjesnik – Technical Gazette, 23, 5, 1365–1372, doi: 10.17559/TV-20150328135652.

Glowacz A. (2018), Acoustic-based fault diagnosis of commutator motor, Electronics, 7, 11, 299, doi: 10.3390/electronics7110299.

Glowacz A., Glowacz W., Glowacz Z., Kozik J. (2018), Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals, Measurement, 113, 1–9, doi: 10.1016/j.measurement.2017.08.036.

Glowacz A., Glowacz Z. (2017a), Diagnosis of stator faults of the single-phase induction motor using acoustic signals, Applied Acoustics. Elsevier, 117, 20–27, doi: 10.1016/j.apacoust.2016.10.012.

Glowacz A., Glowacz Z. (2017b), Recognition of rotor damages in a DC motor using acoustic signals, Bulletin of the Polish Academy of Sciences: Technical Sciences, 65, 2, 187–194, doi: 10.1515/bpasts-2017-0023.

Guo L., Rivero D., Seoane J.A., Pazos A. (2009), Classification of EEG signals using relative wavelet energy and artificial neural networks, [in:] Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation – GEC ’09, New York, New York, USA: ACM Press, pp. 177–184, doi: 10.1145/1543834.1543860.

Haroun S., Seghir A.N., Touati S. (2018), Multiple features extraction and selection for detection and classification of stator winding faults, IET Electric Power Applications, 12, 3, 339–346, doi: 10.1049/iet-epa.2017.0457.

Hu C.Z., Yang Q., Huang M.Y., Yan W.J. (2017), Sparse component analysis-based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox, IET Renewable Power Generation, 11, 3, 330–337. doi: 10.1049/iet-rpg.2016.0240.

Kocaman Ç., Özdemir M. (2009), Comparison of statistical methods and wavelet energy coefficients for determining two common PQ disturbances: Sag and swell, [in:] Electrical and Electronics Engineering, 2009. ELECO 2009. International Conference on, pp. I-80-I-84, doi: 10.1109/ELECO.2009.5355235.

Praveenkumar T., Sabhrish B., Saimurugan M., Ramachandran K.I (2018), Pattern recognition based on-line vibration monitoring system for fault diagnosis of automobile gearbox, Measurement, 114, 233–242, doi: 10.1016/j.measurement.2017.09.041.

Rezazadeh Mehrjou M., Mariun N., Misron N., Radzi M., Musa S. (2017), Broken rotor bar detection in LS-PMSM based on startup current analysis using wavelet entropy features, Applied Sciences, 7, 8, 845, doi: 10.3390/app7080845.

Shah A.M., Bhalja B.R. (2016), Fault discrimination scheme for power transformer using random forest technique, IET Generation, Transmission & Distribution, 10, 6, 1431–1439, doi: 10.1049/iet-gtd.2015.0955.

Shensa M.J. (1992), The discrete wavelet transform: wedding the a trous and Mallat algorithms, IEEE Transactions on Signal Processing, 40, 10, 2464–2482, doi: 10.1109/78.157290.

Short R., Fukunaga K. (1981), The optimal distance measure for nearest neighbor classification, IEEE Transactions on Information Theory, 27, 5, 622–627, doi: 10.1109/TIT.1981.1056403.

Singh A., Parey A. (2017), Gearbox fault diagnosis under fluctuating load conditions with independent angular re-sampling technique, continuous wavelet transform and multilayer perceptron neural network, IET Science, Measurement & Technology, 11, 2, 220–225, doi: 10.1049/iet-smt.2016.0291.

Vaimann T., Sobra J., Belahcen A., Rassõlkin A., Rolak M., Kallaste A. (2018), Induction machine fault detection using smartphone recorded audible noise, IET Science, Measurement & Technology, 12, 4, 554–560, doi: 10.1049/iet-smt.2017.0104.

Wang L.-H., Zhao X.P., Wu J.X., Xie Y.Y., Zhang Y.H. (2017), Motor fault diagnosis based on short-time Fourier transform and convolutional neural network, Chinese Journal of Mechanical Engineering, 30, 6, 1357–1368, doi: 10.1007/s10033-017-0190-5.

Zhang C., Peng Z., Chen S., Li Z., Wang J. (2018), A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 232, 2, 369–380, doi: 10.1177/0954406216677102.

DOI: 10.24425/aoa.2019.128490

Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN)