• Acta Optica Sinica
  • Vol. 40, Issue 22, 2212004 (2020)
Yingfu Guo1, Weiming Quan1, Wenyun Wang2、*, Hao Zhou1, and Longzhou Zou1
Author Affiliations
  • 1Electromechanic Engineering College, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
  • 2Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Xiangtan, Hunan 411201, China
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    DOI: 10.3788/AOS202040.2212004 Cite this Article Set citation alerts
    Yingfu Guo, Weiming Quan, Wenyun Wang, Hao Zhou, Longzhou Zou. Crack Diagnosis Method of Wind Turbine Blade Based on Convolution Neural Network with 3D Vibration Information Fusion[J]. Acta Optica Sinica, 2020, 40(22): 2212004 Copy Citation Text show less
    References

    [1] Joshuva A, Sugumaran V. A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features[J]. Measurement, 152, 107295(2020).

    [2] Asghar A B, Liu X D. Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine[J]. Neurocomputing, 272, 495-504(2018).

    [3] Chen C Z, Wang L L, Zhou B et al. Study on microcrack of wind turbine blade based on infrared thermography technology[J]. Acta Energiae Solaris Sinica, 40, 417-421(2019).

    [5] Li S H, Cai L M. Fan blade crack fault diagnosis based on the analysis of pneumatic signals[J]. Journal of Vibration and Shock, 36, 227-231(2017).

    [6] Jiang M, Zhang W, Wu J G et al. A crack location method for blades via nonlinearity estimation of vibration response[J]. Mechanical Science and Technology for Aerospace Engineering, 37, 545-552(2018).

    [7] Geng X F, Wei K X, Wang Q et al. Crack detection method for wind turbine blades based on the method of multi-frequency harmonic modulation[J]. Journal of Vibration and Shock, 37, 201-205(2018).

    [8] Soualhi A, Medjaher K, Zerhouni N. Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression[J]. IEEE Transactions on Instrumentation and Measurement, 64, 52-62(2015).

    [9] Zhao R, Wang D Z, Yan R Q et al. Machine health monitoring using local feature-based gated recurrent unit networks[J]. IEEE Transactions on Industrial Electronics, 65, 1539-1548(2018).

    [10] Song L Y, Wang H Q, Chen P. Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery[J]. IEEE Transactions on Instrumentation and Measurement, 67, 1887-1899(2018).

    [11] Li Y B, Xu M Q, Liang X H et al. Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings[J]. IEEE Transactions on Industrial Electronics, 64, 6506-6517(2017).

    [12] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

    [13] Chen Z, Li C, Sanchez R. Gearbox fault identification and classification with convolutional neural networks[J]. Shock and Vibration, 2015, 1-10(2015).

    [14] Wang J J, Zhuang J F, Duan L X et al. A multi-scale convolution neural network for featureless fault diagnosis[C]∥2016 International Symposium on Flexible Automation (ISFA). August 1-3, 2016, Cleveland, OH, USA., 65-70(2016).

    [15] Oberholster A J, Heyns P S. On-line fan blade damage detection using neural networks[J]. Mechanical Systems and Signal Processing, 20, 78-93(2006).

    [16] Liu J, Hu Y M, Wang Y et al. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis[J]. Measurement Science and Technology, 29, 055103(2018).

    [17] Gunerkar R S, Jalan A K. Classification ofball bearing faults using vibro-acoustic sensor data fusion[J]. Experimental Techniques, 43, 635-643(2019).

    [18] Chen Z Y, Li W H. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network[J]. IEEE Transactions on Instrumentation and Measurement, 66, 1693-1702(2017).

    [19] Zhu D C, Zhang Y X, Pan Y Y et al. Fault diagnosis for rolling element bearings based on multi-sensor signals and CNN[J]. Journal of Vibration and Shock, 39, 172-178(2020).

    [20] Yan J, Ye N, Li T H et al. Research and implementation of industrial photogrammetry without coded points[J]. Acta Optica Sinica, 39, 1015002(2019).

    [21] Wang W Y, Chen A H. Target-less approach of vibration measurement with virtual points constructed with cross ratios[J]. Measurement, 151, 107238(2020).

    [22] Aghdam H H, Heravi E J, Puig D. Recognizing traffic signs using a practical deep neural network[J]. Robot 2015: Second Iberian Robotics Conference, 399-410(2016).

    [23] Zhang Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1330-1334(2000).

    [24] Hara K, Saito D, Shouno H. Analysis of function of rectified linear unit used in deep learning[C]∥2015 International Joint Conference on Neural Networks (IJCNN). July 12-17, 2015, Killarney, Ireland., 1-8(2015).

    Yingfu Guo, Weiming Quan, Wenyun Wang, Hao Zhou, Longzhou Zou. Crack Diagnosis Method of Wind Turbine Blade Based on Convolution Neural Network with 3D Vibration Information Fusion[J]. Acta Optica Sinica, 2020, 40(22): 2212004
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