One-shot neural architecture search for fault diagnosis using vibration signals
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Yang Hu | Mingtao Li | Jianhua Zheng | Xudong Li | Wenzhen Ma | Xudong Li | Yang Hu | Mingtao Li | Jianhua Zheng | Wenzhen Ma
[1] Kun Jiang,et al. A deep capsule neural network with stochastic delta rule for bearing fault diagnosis on raw vibration signals , 2019 .
[2] Wenlong Li,et al. Fault Diagnosis of Motor Bearings Based on a One-Dimensional Fusion Neural Network , 2019, Sensors.
[3] Haidong Shao,et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders , 2018 .
[4] Ruyi Huang,et al. Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis , 2019, IEEE Access.
[5] Xiang Li,et al. Deep residual learning-based fault diagnosis method for rotating machinery. , 2019, ISA transactions.
[6] Ruqiang Yan,et al. Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study , 2020, ISA transactions.
[7] Shaojiang Dong,et al. Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis , 2021, IEEE Transactions on Industrial Electronics.
[8] Huijun Gao,et al. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis , 2019, Neurocomputing.
[9] Thomas G. Habetler,et al. Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review , 2019, ArXiv.
[10] Alok Aggarwal,et al. Regularized Evolution for Image Classifier Architecture Search , 2018, AAAI.
[11] Shuilong He,et al. Differentiable neural architecture search augmented with pruning and multi-objective optimization for time-efficient intelligent fault diagnosis of machinery , 2021 .
[12] Ming Zhao,et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .
[13] Gaoliang Peng,et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.
[14] Wei Jiang,et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks , 2018, Neurocomputing.
[15] Junsheng Cheng,et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis , 2019, Neurocomputing.
[16] Quoc V. Le,et al. Large-Scale Evolution of Image Classifiers , 2017, ICML.
[17] David He,et al. Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning , 2020, Chinese Journal of Aeronautics.
[18] Robert X. Gao,et al. Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.
[19] Wengang Zhou,et al. A Heterogeneous Graph Embedding Framework for Location-Based Social Network Analysis in Smart Cities , 2020, IEEE Transactions on Industrial Informatics.
[20] Ran Zhang,et al. Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions , 2017, IEEE Access.
[21] Boualem Boashash,et al. 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data , 2018, Neurocomputing.
[22] Yong Zhu,et al. Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery , 2020, IEEE Access.
[23] Liang Gao,et al. A new subset based deep feature learning method for intelligent fault diagnosis of bearing , 2018, Expert Syst. Appl..
[24] Bo Zhang,et al. Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search , 2020, ECCV.
[25] Quoc V. Le,et al. BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models , 2020, ECCV.
[26] Elliot Meyerson,et al. Evolving Deep Neural Networks , 2017, Artificial Intelligence in the Age of Neural Networks and Brain Computing.
[27] Xiangyu Zhang,et al. Single Path One-Shot Neural Architecture Search with Uniform Sampling , 2019, ECCV.
[28] Wei Zhang,et al. Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation , 2018, Journal of Intelligent Manufacturing.
[29] M. Kendall. A NEW MEASURE OF RANK CORRELATION , 1938 .
[30] Ruixin Wang,et al. A reinforcement neural architecture search method for rolling bearing fault diagnosis , 2020 .
[31] Liang Guo,et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.
[32] Jiawei Xiang,et al. Latest developments in gear defect diagnosis and prognosis: A review , 2020 .
[33] Xu Li,et al. Machinery fault diagnosis with imbalanced data using deep generative adversarial networks , 2020 .
[34] Cheng Cheng,et al. A general end-to-end diagnosis framework for manufacturing systems , 2019 .
[35] Yaguo Lei,et al. Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .
[36] Chao Liu,et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults , 2019, Knowl. Based Syst..
[37] Jianbo Yu,et al. A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis , 2019, Comput. Ind..