Abstract
The wear fault of the inner and outer race of bearing in a wind turbine generator system is vital as the performance of bearing effects the transmission efficiency. The accelerometer is not suggested be installed inside the wind turbine generator system since it would damage the structural reliability. This paper proposes a load demodulation normalization framework to detect the wear fault of bearing from electricity-related signals. First, according to the mathematical model of the generator in the three-phase stationary coordinates, this paper selects the stator current as the monitored signals. Second, synchronize the sampled signals in the time domain with synchronous re-sampling. It improves the definition of time-frequency representations (TFRs) of wear fault, and thus avoids the phenomenon of difficulty to determine the resolution of TFRs caused by load fluctuations. The short-time Fourier transform (STFT) is then applied directly to convert the angularly spaced signals into the TFRs. Finally, to improve the precision of classification, this paper proposes an adapted convolutional neural network (CNN) with dropout optimization to classify the wear of bearing. The proposed framework is verified on the motor drive-train platform. The experimental results show that the proposed method has a higher fault detection efficiency than the other methods.
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References
Yang, W., Lang, Z.Q., Tian, W.: Condition monitoring and damage location of wind turbine blades by frequency response transmissibility analysis. IEEE Trans. Industr. Electron. 62, 6558–6564 (2015)
Yang, W., Peng, Z., Wei, K., Shi, P., Tian, W.: Superiorities of variational mode decomposition over empirical mode decomposition particularly in time-frequency feature extraction and wind turbine condition monitoring. IET Renew. Power Gener. 11, 443–452 (2017)
Yang, W., Tavner, P.J., Tian, W.: Wind turbine condition monitoring based on an improved spline-kernelled chirplet transform. IEEE Trans. Industr. Electron. 62, 6565–6574 (2015)
Yan, X., Liu, Y., Zhang, W., Jia, M., Wang, X.: Research on a novel improved adaptive variational mode decomposition method in rotor fault diagnosis. Appl. Sci. 10, 1696 (2020)
Yang, W., Peng, Z., Wei, K., Tian, W.: Structural health monitoring of composite wind turbine blades: challenges, issues and potential solutions. IET Renew. Power Gener. 11, 411–416 (2017)
Wang, D., Tsui, K.-L.: Statistical modeling of bearing degradation signals. IEEE Trans. Reliab. 66, 1331–1344 (2017)
Li, W., Xie, Z., Wong, P.K., Mei, X., Zhao, J.: Adaptive-event-trigger-based fuzzy nonlinear lateral dynamic control for autonomous electric vehicles under insecure communication networks. IEEE Trans. Industr. Electron. 68, 2447–2459 (2020)
Wang, X.-B., Yang, Z.-X., Wong, P.K., Deng, C.: Novel paralleled extreme learning machine networks for fault diagnosis of wind turbine drivetrain. Memetic Comp. 11, 127–142 (2019). https://doi.org/10.1007/s12293-018-0277-2
Zhao, J., Wong, P.K., Ma, X., Xie, Z., Xu, J., Cristino, V.A.M.: Simplification of finite element modeling for plates structures with constrained layer damping by using single-layer equivalent material properties. Compos. Part B-Eng. 157, 283–288 (2019)
Liang, Z., Zhao, J., Dong, Z., Wang, Y., Ding, Z.: Torque Vectoring and Rear-Wheel-Steering Control for Vehicle’s Uncertain Slips on Soft and Slope Terrain Using Sliding Mode Algorithm. IEEE Trans. Veh. Technol. 69, 3805–3815 (2020)
Ning, D., Sun, S., Du, H., Li, W., Zhang, N., Zheng, M., Luo, L.: An electromagnetic variable inertance device for seat suspension vibration control. Mech. Syst. Signal Process. 133, 106259 (2019)
Fu, W., Wang, K., Zhang, C., Tan, J.: A hybrid approach for measuring the vibrational trend of hydroelectric unit with enhanced multi-scale chaotic series analysis and optimized least squares support vector machine. Trans. Inst. Meas. Control. 41, 4436–4449 (2019)
Wu, J., Wu, C., Cao, S., Or, S.W., Deng, C., Shao, X.: Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines. IEEE Trans. Industr. Electron. 66, 529–539 (2018)
Cheng, Y., Zhu, H., Wu, J., Shao, X.: Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Trans. Industr. Inf. 15, 987–997 (2018)
Liang, P., Deng, C., Wu, J., Yang, Z., Zhu, J., Zhang, Z.: Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. Comput. Ind. 113, 103132 (2019). https://doi.org/10.1016/j.compind.2019.103132
Wang, X.-B., Yang, Z.-X., Yan, X.-A.: Novel particle swarm optimization-based variational mode decomposition method for the fault diagnosis of complex rotating machinery. IEEE/ASME Trans. Mechatron 23, 68–79 (2017)
Zhong, J.-H., Wong, P.K., Yang, Z.-X.: Fault diagnosis of rotating machinery based on multiple probabilistic classifiers. Mech. Syst. Signal Process. 108, 99–114 (2018). https://doi.org/10.1016/j.ymssp.2018.02.009
Yang, Z.-X., Wang, X.-B., Zhong, J.-H.: Representational learning for fault diagnosis of wind turbine equipment: a multi-layered extreme learning machines approach. Energies 9, 379 (2016). https://doi.org/10.3390/en9060379
Lu, D., Qiao, W., Gong, X.: Current-based gear fault detection for wind turbine gearboxes. IEEE Trans. Sustain. Energy. 8, 1453–1462 (2017). https://doi.org/10.1109/TSTE.2017.2690835
Acknowledgment
This work was funded in part by the Science and Technology Development Fund, Macau SAR (File no. 0018/2019/AKP, 0008/2019/AGJ, FDCT/194/2017/A3, and SKL-IOTSC-2018–2020), in part by the University of Macau under Grant MYRG2018–00248-FST and MYRG2019–0137-FST.
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Luo, Z., Wang, XB., Yang, ZX. (2021). Fault Representations of Bearing Race Based on Grayscale Maps and CNN Networks. In: Zhen, D., et al. Proceedings of IncoME-V & CEPE Net-2020. IncoME-V 2020. Mechanisms and Machine Science, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-75793-9_7
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