Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network
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Lin Bo | Chang Peng | Honglin Luo | Dongming Hou | Lin Bo | Chang Peng | Dongming Hou | Hongling Luo
[1] Yu Yang,et al. Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data , 2020 .
[2] Guiming Mei,et al. A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction , 2016 .
[3] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[4] Huadong Ma,et al. MCFF-CNN: Multiscale comprehensive feature fusion convolutional neural network for vehicle color recognition based on residual learning , 2020, Neurocomputing.
[5] Lei Deng,et al. Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine , 2014 .
[6] Haidong Shao,et al. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine , 2018, Knowl. Based Syst..
[7] Steven Verstockt,et al. Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .
[8] Mayorkinos Papaelias,et al. Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals , 2016 .
[9] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[10] Konstantinos Gryllias,et al. Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine , 2019, Mechanical Systems and Signal Processing.
[11] Wentao Mao,et al. Online detection of bearing incipient fault with semi-supervised architecture and deep feature representation , 2020 .
[12] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[13] Robert Sabourin,et al. A classifier fusion system for bearing fault diagnosis , 2013, Expert Syst. Appl..
[14] V. Purushotham,et al. Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition , 2005 .
[15] Dong Wang,et al. EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings , 2018, Sensors.
[16] Zitong Zhou,et al. A Compact Convolutional Neural Network Augmented with Multiscale Feature Extraction of Acquired Monitoring Data for Mechanical Intelligent Fault Diagnosis , 2020 .
[17] Haidong Shao,et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders , 2018 .
[18] Guanghua Xu,et al. Health indicator construction of machinery based on end-to-end trainable convolution recurrent neural networks , 2020 .
[19] Mien Van,et al. Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier , 2020, Sensors.
[20] Robert B. Randall,et al. Intelligent diagnosis of bearing knock faults in internal combustion engines using vibration simulation , 2016 .
[21] Qiao Hu,et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs , 2007 .
[22] Junsheng Cheng,et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis , 2019, Neurocomputing.
[23] Dazhong Wu,et al. Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.
[24] Jianxin Liu,et al. Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter , 2018 .
[25] Shuhui Wang,et al. Convolutional neural network-based hidden Markov models for rolling element bearing fault identification , 2017, Knowl. Based Syst..
[26] Brigitte Chebel-Morello,et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .
[27] Ching-Hung Lee,et al. Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function , 2020, Sensors.
[28] Kai Sun,et al. Multiscale dense convolutional neural network for DSA cerebrovascular segmentation , 2020, Neurocomputing.
[29] Davide Anguita,et al. Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project , 2016 .
[30] Haidong Shao,et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing , 2018 .
[31] Yaguo Lei,et al. Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .
[32] Yaguo Lei,et al. EEMD method and WNN for fault diagnosis of locomotive roller bearings , 2011, Expert Syst. Appl..
[33] Jianhui Lin,et al. Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD , 2015, Sensors.
[34] Ilaria Corni,et al. Observing early stage rail-axle bearing damage , 2015 .