Deep Reinforcement Learning-Based Online Domain Adaptation Method for Fault Diagnosis of Rotating Machinery
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X. Shao | Jun Wu | Xuebing Xu | Guoqiang Li | C. Deng
[1] Xinyu Shao,et al. Autoencoder Quasi-Recurrent Neural Networks for Remaining Useful Life Prediction of Engineering Systems , 2021, IEEE/ASME Transactions on Mechatronics.
[2] Chao Deng,et al. Convolutional Neural Network-Based Bayesian Gaussian Mixture for Intelligent Fault Diagnosis of Rotating Machinery , 2021, IEEE Transactions on Instrumentation and Measurement.
[3] Xinyu Shao,et al. Remaining Useful Life Prognosis Based on Ensemble Long Short-Term Memory Neural Network , 2021, IEEE Transactions on Instrumentation and Measurement.
[4] Haidong Shao,et al. Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions , 2020, Knowl. Based Syst..
[5] Xinyu Shao,et al. Ensemble Generalized Multiclass Support-Vector-Machine-Based Health Evaluation of Complex Degradation Systems , 2020, IEEE/ASME Transactions on Mechatronics.
[6] Thomas Parisini,et al. State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section , 2020, IEEE/ASME Transactions on Mechatronics.
[7] M.M.A. Mahfouz,et al. A protection scheme for multi-distributed smart microgrid based on auto-cosine similarity of feeders current patterns , 2020 .
[8] Zheng Liu,et al. Intelligent Fault Diagnosis of Multichannel Motor–Rotor System Based on Multimanifold Deep Extreme Learning Machine , 2020, IEEE/ASME Transactions on Mechatronics.
[9] Albert Y. Zomaya,et al. Reinforcement learning in sustainable energy and electric systems: a survey , 2020, Annu. Rev. Control..
[10] Xinyu Shao,et al. Reliability prediction of machinery with multiple degradation characteristics using double-Wiener process and Monte Carlo algorithm , 2019 .
[11] Kun Jiang,et al. A deep capsule neural network with stochastic delta rule for bearing fault diagnosis on raw vibration signals , 2019 .
[12] Xiang Li,et al. Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks , 2019, IEEE Transactions on Industrial Electronics.
[13] Haoxiang Lang,et al. Adaptive System Identification and Severity Index-Based Fault Diagnosis in Motors , 2019, IEEE/ASME Transactions on Mechatronics.
[14] Cheng Wu,et al. Domain Space Transfer Extreme Learning Machine for Domain Adaptation , 2019, IEEE Transactions on Cybernetics.
[15] Chao Liu,et al. An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems , 2019, Mechanical Systems and Signal Processing.
[16] Jun Wu,et al. Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks , 2019, IEEE Transactions on Industrial Informatics.
[17] Chao Liu,et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults , 2019, Knowl. Based Syst..
[18] Zhencai Zhu,et al. A New Piezoelectric Bimorph Energy Harvester Based on the Vortex-Induced-Vibration Applied in Rotational Machinery , 2019, IEEE/ASME Transactions on Mechatronics.
[19] Huijun Gao,et al. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis , 2019, Neurocomputing.
[20] Robert X. Gao,et al. Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.
[21] Liang Gao,et al. A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[22] Chao Liu,et al. Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application , 2018, ISA transactions.
[23] Mevludin Glavic,et al. (Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives , 2019, Annu. Rev. Control..
[24] Min Xia,et al. Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks , 2018, IEEE/ASME Transactions on Mechatronics.
[25] Sergey Levine,et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..
[26] M. Deisenroth,et al. Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.
[27] Geoffrey E. Hinton,et al. Dynamic Routing Between Capsules , 2017, NIPS.
[28] Chen Lu,et al. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification , 2017, Adv. Eng. Informatics.
[29] Peng Wang,et al. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox , 2017, Sensors.
[30] Moncef Gabbouj,et al. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.
[31] Cong Wang,et al. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings , 2016 .
[32] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[33] Walter Sextro,et al. Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification , 2016, PHM Society European Conference.
[34] Haidong Shao,et al. Rolling bearing fault diagnosis using an optimization deep belief network , 2015 .
[35] Noureddine Zerhouni,et al. Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.
[36] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[37] Ruoyu Li,et al. Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach , 2013, IEEE Transactions on Industrial Electronics.
[38] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.