DWT-PSD extraction feature for defects diagnosis of small wind generator
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Applied Automation & Industrial Diagnostics Laboratory Djelfa University
Submission date: 2019-04-09
Final revision date: 2019-06-29
Acceptance date: 2019-07-03
Online publication date: 2019-07-04
Publication date: 2019-07-04
Corresponding author
Lahcène Noureddine   

Applied Automation & Industrial Diagnostics Laboratory Djelfa University
Diagnostyka 2019;20(3):45–52
In this work, the ability to detect broken rotor bar defects in small renewable energy system based on squirrel cage induction generator (SCIG), using a digital signal processing of the captured phase currents is presented. The new suggested approach in this study is the combination of two techniques, the first technique is the discrete wavelet transform by decomposition the phase current signal in multilevel frequency bands, with the analysis of some selected approximations and/or details, which containing the both lower and upper sideband components presenting the characteristic frequency of BRB fault, and the second one is the power spectral density (PSD). This approach gives the ability of optimizing the diagnosis of rotor defects in electrical generators. The DWT-PSD results given from the suggested approach are proved and improved by comparing them with the results of PSD obtained from original phase current signal delivered by 5.7 kW squirrel cage induction generator based on small wind energy conversion system.
Pons Llinares J, Climente Alarcón V, Vedreño Santos F, Antonino Daviu J, RieraGuasp M. Electric machines diagnosis techniques via transient current analysis. Proceedings of the 38th Annual Conference of the IEEE Industrial Electronics Society, IECON 2012, 25-28 October, Montreal, Canada.
Kia SH, Henao H, Capolino GA. Efficient digital signal processing techniques for induction machine fault diagnosis. Proc. IEEE Workshop Electrical Machines Design, Control and Diagnosis, WEMDCD 2013, Mar. 11–12, Paris, France.
Thomson WT, Fenger M. Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine 2001;26-34.
Cai H, Sun Q, Wood D. Condition monitoring and fault diagnosis of a small permanent magnet generator. Wind engineering. 2016;40(3), 270-282. https://doi.org/10.1177/030952....
Castellani F, Astolfi D, Becchetti M. Berno F. Experimental damage detection on small wind turbines through vibration and acoustic analysis. Proceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics, 2018:4793-4807.
Jinglong Chen, Jun Pan, Zipeng Li, Yanyang Zi, Xuefeng Chen. Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals. Renewable Energy. 2016;89:80-92. https://doi.org/10.1016/j.rene....
Wei Teng, Xian Ding, Hao Cheng, Chen Han, Yibing Liu, Haihua Mu. Compound fault diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform. Renewable Energy, 2019;136:393-402. https://doi.org/10.1016/j.rene....
Jianming Ding, Chengcheng Ding. Automatic detection of a wheelset bearing fault using a multi-level empirical wavelet transform. Measurement. 2019;134: 179-192. https://doi.org/10.1016/j.meas....
Dong Wang, Kwok-Leung Tsui, Yong Qin. Optimization of segmentation fragments in empirical wavelet transform and its applications to extracting industrial bearing fault features. Measurement, 2019;133:328-340. https://doi.org/10.1016/j.meas....
Baojia Chen, Baoming Shen, Fafa Chen, Hongliang Tian, Wenrong Xiao, Fajun Zhang, Chunhua Zhao. fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing. Measurement. 2019;131:400-411. https://doi.org/10.1016/j.meas....
Jaskaran Singh, Darpe AK, Singh SP. Rolling element bearing fault diagnosis based on Over-Complete rational dilation wavelet transform and auto-correlation of analytic energy operator. Mechanical Systems and Signal Processing. 2018;100:662-693. https://doi.org/10.1016/j.ymss....
Ping Ma, Hongli Zhang, Wenhui Fan, Cong Wang. Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform. Measurement, In press, accepted manuscript, 2019;l137: 189-202 https://doi.org/10.1016/j.meas....
Xin Zhang, Zhiwen Liu, Jiaxu Wang, Jinglin Wang. Time–frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets. ISA Transactions. 2019;87:225-234. https://doi.org/10.1016/j.isat....
Yan R, Gao RX, Chen X. Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Processing 2014; 96:1–15.
Antonino Daviu JA, Riera Guasp M, Pineda Sánchez M, Pérez R B. A critical comparison between DWT and Hilbert–Huang-Based methods for the diagnosis of rotor bar failures in induction machines. IEEE Transactions on Industry Applications 2009;45:1794–1803.
Daubechies I. Ten lectures on wavelets. SIAM, Philadelphia, PA. 1992.
Mallat S. A wavelet tour of signal processing. 2nd. Edition, Academic Press, San Diego, California, USA. 1999.
Strang G, Nyugen T. Wavelets and Filter Banks, Wellesley Cambridge Press, Wellesley, MA, USA, 1996.
Qian, S. Introduction to Time-Frequency and Wavelet Transforms, Prentice Hall PTR, 2002.
Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989;11(7): 674–693.
Rafiee J, Rafiee MA, Tse PW. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications, 2010; 37(6): 4568–4579.
Lahcène Noureddine, Touhami O. Diagnosis of wind energy system faults Part I : Modeling of the squirrel cage induction generator. International Journal of Advanced Computer Science and Application (IJACSA) 2015;6:46-53.
Daviu JA, Riera Guasp M, Roger Folch J, Martínez Giménez F, Peris A. Application and optimization of the discrete wavelet transform for the detection of broken rotor bars in induction machines. Appl. Comput. Harmon. Anal. 2006;21:268–279.