Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

Articles citing Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

Title / Author	Year
Deep learning and its applications to machine health monitoring R Zhao, R Yan, Z Chen, K Mao, P Wang, RX Gao Mechanical Systems and Signal Processing 115, 213-237	2019
Deep learning for smart manufacturing: Methods and applications J Wang, Y Ma, L Zhang, RX Gao, D Wu Journal of Manufacturing Systems 48, 144-156	2018
A new convolutional neural network-based data-driven fault diagnosis method L Wen, X Li, L Gao, Y Zhang IEEE Transactions on Industrial Electronics 65 (7), 5990-5998	2017
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks O Abdeljaber, O Avci, S Kiranyaz, M Gabbouj, DJ Inman Journal of Sound and Vibration 388, 154-170	2017
A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals W Zhang, G Peng, C Li, Y Chen, Z Zhang Sensors 17 (2), 425	2017
An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox L Jing, T Wang, M Zhao, P Wang Sensors 17 (2), 414	2017
Deep learning and its applications to machine health monitoring: A survey R Zhao, R Yan, Z Chen, K Mao, P Wang, RX Gao arXiv preprint arXiv:1612.07640	2016
Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox G Jiang, H He, J Yan, P Xie IEEE Transactions on Industrial Electronics 66 (4), 3196-3207	2018
An automated system for epilepsy detection using EEG brain signals based on deep learning approach I Ullah, M Hussain, H Aboalsamh Expert Systems with Applications 107, 61-71	2018
Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes M Zhao, M Kang, B Tang, M Pecht IEEE Transactions on Industrial Electronics 65 (5), 4290-4300	2017
Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning J Sun, C Yan, J Wen IEEE Transactions on Instrumentation and Measurement 67 (1), 185-195	2017
LiftingNet: A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification J Pan, Y Zi, J Chen, Z Zhou, B Wang IEEE Transactions on Industrial Electronics 65 (6), 4973-4982	2017
Sensor fault classification based on support vector machine and statistical time-domain features SU Jan, YD Lee, J Shin, I Koo IEEE Access 5, 8682-8690	2017
1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data O Abdeljaber, O Avci, MS Kiranyaz, B Boashash, H Sodano, DJ Inman Neurocomputing 275, 1308-1317	2018
An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis S Li, G Liu, X Tang, J Lu, J Hu Sensors 17 (8), 1729	2017
A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals H Wang, S Li, L Song, L Cui Computers in Industry 105, 182-190	2019
Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning P Cao, S Zhang, J Tang Ieee Access 6, 26241-26253	2018
Convolutional neural network-based hidden Markov models for rolling element bearing fault identification S Wang, J Xiang, Y Zhong, Y Zhou Knowledge-Based Systems 144, 65-76	2018
A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning X Li, W Zhang, Q Ding Neurocomputing 310, 77-95	2018
Estimation of bearing remaining useful life based on multiscale convolutional neural network J Zhu, N Chen, W Peng IEEE Transactions on Industrial Electronics 66 (4), 3208-3216	2018

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