Acta Optica Sinica, Volume. 40, Issue 22, 2212004(2020)
Crack Diagnosis Method of Wind Turbine Blade Based on Convolution Neural Network with 3D Vibration Information Fusion
[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).
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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
Category: Instrumentation, Measurement and Metrology
Received: Jun. 23, 2020
Accepted: Aug. 3, 2020
Published Online: Oct. 25, 2020
The Author Email: Wang Wenyun (wwy73210693@163.com)