Sparse dictionary learning based adversarial variational auto-encoders for fault identification of wind turbines
暂无分享,去创建一个
Wei Teng | Zhiyong Ma | Xin Wu | Liu Xiaobo | Shiming Wu | Yibing Liu | Xin Wu | Yibing Liu | Zhiyong Ma | Liu Xiaobo | W. Teng | Shiming Wu | Xiaobo Liu
[1] Jin Zhu,et al. Wind turbine health state monitoring based on a Bayesian data-driven approach , 2018, Renewable Energy.
[2] Shakir Mohamed,et al. Variational Approaches for Auto-Encoding Generative Adversarial Networks , 2017, ArXiv.
[3] Yang Lyu,et al. A Generic Anomaly Detection of Catenary Support Components Based on Generative Adversarial Networks , 2020, IEEE Transactions on Instrumentation and Measurement.
[4] Diego Cabrera,et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals , 2016 .
[5] Yixiang Huang,et al. Adaptive feature extraction using sparse coding for machinery fault diagnosis , 2011 .
[6] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[7] Deyi Xue,et al. Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network , 2021, Renewable Energy.
[8] Zhenan Sun,et al. A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications , 2020, IEEE Transactions on Knowledge and Data Engineering.
[9] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[10] Fang Ruiming,et al. Identifying early defects of wind turbine based on SCADA data and dynamical network marker , 2020 .
[11] Chao Liu,et al. An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring , 2018, Renewable Energy.
[12] Jin Chen,et al. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model , 2016 .
[13] Yan Song,et al. Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network , 2020, IEEE Transactions on Industrial Informatics.
[14] Dongsheng Li,et al. Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data , 2017 .
[15] D. Coronado,et al. Assessment and Validation of Oil Sensor Systems for On-line Oil Condition Monitoring of Wind Turbine Gearboxes , 2014 .
[16] Nick Barnes,et al. Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[17] Fredrik Sandin,et al. Online feature learning for condition monitoring of rotating machinery , 2017, Eng. Appl. Artif. Intell..
[18] Sofiane Achiche,et al. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description , 2013, Appl. Soft Comput..
[19] Wei Qiao,et al. A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods , 2015, IEEE Transactions on Industrial Electronics.
[20] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[21] Lina Yao,et al. Adversarial Variational Embedding for Robust Semi-supervised Learning , 2019, KDD.
[22] Huan Long,et al. Wind Turbine Gearbox Failure Identification With Deep Neural Networks , 2017, IEEE Transactions on Industrial Informatics.
[23] Wei Qiao,et al. Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes , 2019, IEEE Transactions on Industrial Electronics.
[24] Shunming Li,et al. Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks , 2019, IEEE Access.
[25] Georg Langs,et al. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.
[26] Aijun Hu,et al. Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism , 2021 .
[27] Andrew Kusiak,et al. Application of cyclic coherence function to bearing fault detection in a wind turbine generator under electromagnetic vibration , 2017 .
[28] David Infield,et al. SCADA‐based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes , 2018, IET Renewable Power Generation.
[29] Hui Li,et al. A probability evaluation method of early deterioration condition for the critical components of wind turbine generator systems , 2016 .
[30] Wenjing Hu,et al. Anomaly detection and fault analysis of wind turbine components based on deep learning network , 2018, Renewable Energy.
[31] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[32] Q. H. Wu,et al. A Data-Driven Residual-Based Method for Fault Diagnosis and Isolation in Wind Turbines , 2019, IEEE Transactions on Sustainable Energy.
[33] Xiaoyang Liu,et al. FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults , 2020, IEEE Transactions on Industrial Informatics.
[34] Zijun Zhang,et al. Wind Turbine Blade Breakage Monitoring With Deep Autoencoders , 2018, IEEE Transactions on Smart Grid.
[35] Simon J. Watson,et al. Using SCADA data for wind turbine condition monitoring – a review , 2017 .
[36] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[37] Joseph F. Murray,et al. Dictionary Learning Algorithms for Sparse Representation , 2003, Neural Computation.
[38] Qinkai Han,et al. Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review , 2019, Mechanical Systems and Signal Processing.
[39] Li Zhang,et al. Structured Latent Label Consistent Dictionary Learning for Salient Machine Faults Representation-Based Robust Classification , 2017, IEEE Transactions on Industrial Informatics.
[40] Bongtae Han,et al. Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines , 2016 .
[41] Yibing Liu,et al. DNN‐based approach for fault detection in a direct drive wind turbine , 2018, IET Renewable Power Generation.
[42] Gang Hua,et al. CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[43] Meik Schlechtingen,et al. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples , 2014, Appl. Soft Comput..
[44] Sebastian Nowozin,et al. Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks , 2017, ICML.
[45] Yupu Yang,et al. Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring , 2019, Journal of Process Control.
[46] Wei Qiao,et al. Fault Prognosis and Remaining Useful Life Prediction of Wind Turbine Gearboxes Using Current Signal Analysis , 2018, IEEE Transactions on Sustainable Energy.
[47] Jean Ponce,et al. Task-Driven Dictionary Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Yang Tang,et al. Multimode Process Monitoring and Fault Detection: A Sparse Modeling and Dictionary Learning Method , 2017, IEEE Transactions on Industrial Electronics.
[49] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[50] Naixue Xiong,et al. Learning Sparse Representation With Variational Auto-Encoder for Anomaly Detection , 2018, IEEE Access.
[51] Yingning Qiu,et al. Diagnosis of wind turbine faults with transfer learning algorithms , 2021 .
[52] Toby P. Breckon,et al. GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training , 2018, ACCV.