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Warianty tytułu
Języki publikacji
Abstrakty
A fault diagnosis method for the rotating rectifier of a brushless three-phase synchronous aerospace generator is proposed in this article. The proposed diagnostic system includes three steps: data acquisition, feature extraction and fault diagnosis. Based on a dynamic Fast Fourier Transform (FFT), this method processes the output voltages of aerospace generator continuously and monitors the continuous change trend of the main frequency in the spectrum before and after the fault. The trend can be used to perform fault diagnosis task. The fault features of the rotating rectifier proposed in this paper can quickly and effectively distinguish single and double faulty diodes. In order to verify the proposed diagnosis system, simulation and practical experiments are carried out in this paper, and good results can be achieved.
Czasopismo
Rocznik
Tom
Strony
269--288
Opis fizyczny
Bibliogr. 29 poz., rys., tab., wykr., wzory
Twórcy
autor
- Nanjing University of Aeronautics and Astronautics, College of Automation Engineering, Nanjing City, Jiangsu Province, 211100, China
autor
- Nanjing University of Aeronautics and Astronautics, College of Automation Engineering, Nanjing City, Jiangsu Province, 211100, China
autor
- Nanjing University of Aeronautics and Astronautics, College of Automation Engineering, Nanjing City, Jiangsu Province, 211100, China
Bibliografia
- [1] Madonna, V., Giangrande, P., & Galea, M. (2018). Electrical power generation in aircraft: review, challenges, and opportunities. IEEE Transactions on Transport Electrification, 4(3), 646-659. https://doi.org/10.1109/TTE.2018.2834142
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- [3] Jiang, S. B., Wong, P. K., Guan, R. C., Liang, Y. C., & Li, J. (2019). An Efficient Fault Diagnostic Method for Three-Phase Induction Motors Based on Incremental Broad Learning and Non-Negative Matrix Factorization. IEEE Access, 4, 17780-17790. https://doi.org/10.1109/ACCESS.2019.2895909
- [4] Jia, Z., Liu, Z. B., Vong, C. M., & Pecht, M. C. (2019). A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images. IEEE Access, 7, 12348-12359. https://doi.org/10.1109/ACCESS.2019.2893331
- [5] Batzel, T. D., & Swanson, D. C. (2009). Prognostic health management of aircraft power generators. IEEE Transactions on Aerospace and Electronic Systems, 45(2), 473-482. https://doi.org/10.1109/TAES.2009.5089535
- [6] Batzel, T. D., Swanson, D. C., & Defenbaugh, J. F. (2003). Predictive diagnostics for the main field winding and rotating rectifier assembly in the brushless synchronous generator. 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, USA, 349-354. https://doi.org/10.1109/DEMPED.2003.1234600
- [7] Cui, J., Tang, J., Shi, G. & Zhang, Z. (2017). Generator rotating rectifier fault detection method based on stacked auto-encoder. IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD), UK, 256-261. https://doi.org/10.1109/WEMDCD.2017.7947756
- [8] Cui, J., Shi, G., & Zhang, Z. (2017). Fault detection of aircraft generator rotating rectifier based on SAE and SVDD method. Prognostics and System Health Management Conference, China, 1-5. https://doi.org/10.1109/PHM.2017.8079202
- [9] Wei, Z., Pang, J., & Sun, C. (2019). Fault diagnosis of rotating rectifier based on waveform distortion and polarity of current. IEEE Transactions on Industry Applications, 55(3), 2356-2367. https://doi.org/10.1109/TIA.2019.2893140
- [10] Sun, S., Wu, Y., Cai, W., & Ding, W. (2017). Fault Diagnosis of Rotating Rectifier Based on Harmonic Features. IOP Conference Series: Materials Science and Engineering, 199, 01246. https://doi.org/10.1088/1757-899X/199/1/012146
- [11] Zhang, Z., Liu, W., Peng, J., Zhao, D., Meng, T., Pang, J., & Sun, C. (2017). Identification of TBAES rotating diode failure. IET Electric Power Applications, 11(2), 260-271. https://doi.org/10.1049/iet-epa.2016.0468
- [12] Bui, H. K., Bracikowski, N., Hecquet, M., Zappellini, K. L., & Ducreux, J. P. (2017). Simulation of a large power brushless synchronous generator (blsg) with a rotating rectifier by a reluctance network for fault analysis and diagnosis. IEEE Transactions on Industry Applications, 53(5), 4327-4337. https://doi.org/10.1109/TIA.2017.2701789
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- [14] Sottile, J., Trutt, F. C., & Leedy, A. W. (2006). Condition Monitoring of brushless three-phase synchronous generators with stator winding or rotor circuit deterioration. IEEE Transactions on Industry Applications, 42(5), 1209-1215. https://doi.org/10.1109/IAS.2001.955747
- [15] Wei, Z., Liu, W., Zhang, Z., Jiao, N., Peng, J., & Meng, T. (2018). Rotating rectifier fault detection method of wound-rotor synchronous starter generator with three-phase exciter. The 4th International Symposium on More Electric Aircraft Technology, China, 524-528. https://doi.org/10.1049/joe.2018.0038
- [16] McArdle, M. G., & Morrow, D. J. (2004). Noninvasive detection of brushless exciter rotating diode failure. IEEE Transactions on Energy Conversion, 19(2), 378-383. https://doi.org/10.1109/TEC.2003.822325
- [17] Mohamed, S., Khmais, B., & Abdelkader, C. (2013). Detection of brushless exciter rotating diodes failures by spectral analysis of main output voltage. International Conference on Electrical Engineering and Software Applications, Tunisia, 1-6. https://doi.org/10.1109/ICEESA.2013.6578469
- [18] Mohamed, S., Khmais, B., Abdelkader, C., & Mohamed, E. (2014). Brushless Three-Phase Synchronous Generator under rotating diode failure conditions. IEEE Transactions on Energy Conversion, 29(3), 594-601. https://doi.org/10.1109/TEC.2014.2312173
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- [23] Cui, J., & Wang, Y. (2011). Analog circuit fault classification using improved one-against-one support vector machines. Metrology and. Measurement Systems, 18(4), 569-582. https://doi.org/10.2478/v10178-011-0055-7
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- [26] Tantawy, A., Koutsoukos, X., & Biswas, G. (2012). Aircraft Power Generators: Hybrid Modeling and Simulation for Fault Detection. IEEE Transactions on Aerospace and Electronic Systems, 48(1), 552-571. https://doi.org/10.1109/TAES.2012.6129655
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Uwagi
1. This work was supported by the Fundamental Research Funds for the Central Universities (grant no. NS2017019).
2. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-72a68649-aaf2-48a2-b42c-bd96b1dd3bce