Online defect recognition of narrow overlap weld based on two-stage recognition model combining continuous wavelet transform and convolutional neural network

Computers in Industry - Tập 112 - Trang 103115 - 2019
Rui Miao1, Yuntian Gao1, Liang Ge1, Zihang Jiang1, Jie Zhang2
1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
2College of Mechanical Engineering,Donghua University,201620, China

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Tài liệu tham khảo

Cha, 2017, Büyüköztürk O. Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks, Comput. Civ. Infrastruct. Eng., 32, 361, 10.1111/mice.12263

Chen, 2015, Multi-layer neural network with deep belief network for gearbox fault diagnosis, J. Vibroengineering, 17, 2379

Chen, 2017, Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network-based health state classification, Adv. Eng. Inform., 32, 139, 10.1016/j.aei.2017.02.005

Cui, 2016

Demetgul, 2014, Fault diagnosis on material handling system using feature selection and data mining techniques, Measurement, 55, 15, 10.1016/j.measurement.2014.04.037

Donahue, 2017, Long-term recurrent convolutional networks for visual recognition and description, IEEE Trans. Pattern Anal. Mach. Intell., 39, 677, 10.1109/TPAMI.2016.2599174

Fu, 2017, Machining vibration states monitoring based on image representation using convolutional neural networks, Eng. Appl. Artif. Intell., 65, 240, 10.1016/j.engappai.2017.07.024

Fu, 2017, Machining vibration states monitoring based on image representation using convolutional neural networks, Eng. Appl. Artif. Intell., 65, 240, 10.1016/j.engappai.2017.07.024

Gan, 2015, Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings, Mech. Syst. Signal Process.

Gao, 2017, Tight butt joint weld detection based on optical flow and particle filtering of magneto-optical imaging, Mech. Syst. Signal Process., 96, 16, 10.1016/j.ymssp.2017.04.001

García-Martín, 2011, Non-Destructive Techniques Based on Eddy Current Testing, Sensors, 11, 2525, 10.3390/s110302525

He, 2014, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell., 37, 1904, 10.1109/TPAMI.2015.2389824

Ioffe, 2015, Batch normalization: accelerating deep network training by reducing internal covariate shift

Jing, 2017, A convolutional neural network-based feature learning and fault diagnosis method for the condition monitoring of gearbox, Measurement Journal of the International Measurement Confederation, 111, 1, 10.1016/j.measurement.2017.07.017

Kuremoto, 2014, Time series forecasting using a deep belief network with restricted Boltzmann machines, Neurocomputing, 137, 47, 10.1016/j.neucom.2013.03.047

Lu, 2017, Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification, Signal Processing, 130, 377, 10.1016/j.sigpro.2016.07.028

Podržaj, 2016, The application of LVQ neural network for weld strength evaluation of RF-Welded plastic materials, Ieee/asme Trans. Mechatron., 21, 1063, 10.1109/TMECH.2015.2498278

Shao, 2018, Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing, Mech. Syst. Signal Process., 100, 743, 10.1016/j.ymssp.2017.08.002

Shao, 2018, Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing, Mech. Syst. Signal Process., 100, 743, 10.1016/j.ymssp.2017.08.002

Simonyan, 2014, Very deep convolutional networks for large-scale image recognition, Comput. Sci.

Sinha, 2005, Spectral decomposition of seismic data with continuous wavelet transform, Geophysics, 70, 10.1190/1.2127113

Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., 11, 3371

Wang, 2018, Convolutional neural network-based hidden Markov models for rolling element bearing fault identification, Knowledge Based Syst., 144, 65, 10.1016/j.knosys.2017.12.027

Wang, 2018, Convolutional neural network-based hidden Markov models for rolling element bearing fault identification, Knowledge Based Syst., 144, 65, 10.1016/j.knosys.2017.12.027

Zhang, 2017, A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load, Mech. Syst. Signal Process., 100, 439, 10.1016/j.ymssp.2017.06.022

Zhiqiang, 2015, Sanchez René-Vinicio. Gearbox Fault Identification and Classification with Convolutional Neural Networks, Shock. Vib., 2015, 1