Deep learning fault diagnosis method based on global optimization GAN for unbalanced data
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Chenglin Wen | Shuai Yang | Hamido Fujita | Funa Zhou | Danmin Chen | H. Fujita | Chenglin Wen | F. Zhou | Shuai Yang | Danmin Chen
[1] Jing Zhang,et al. Large cost-sensitive margin distribution machine for imbalanced data classification , 2017, Neurocomputing.
[2] Lale Özbakir,et al. Cost-sensitive meta-learning classifiers: MEPAR-miner and DIFACONN-miner , 2016, Knowl. Based Syst..
[3] Jun Jo,et al. Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection , 2017, 2017 IEEE International Conference on Big Data (Big Data).
[4] JayHyung Lee,et al. Nonlinear modeling and state estimation for the Tennessee Eastman challenge process , 1995 .
[5] Xiangliang Zhang,et al. An up-to-date comparison of state-of-the-art classification algorithms , 2017, Expert Syst. Appl..
[6] Chenglin Wen,et al. A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery , 2018, Sensors.
[7] Dimitris N. Metaxas,et al. StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[8] Min Chen,et al. Selection-based resampling ensemble algorithm for nonstationary imbalanced stream data learning , 2019, Knowl. Based Syst..
[9] Lale Özbakir,et al. A cost-sensitive classification algorithm: BEE-Miner , 2016, Knowl. Based Syst..
[10] Yide Wang,et al. A M-EKF fault detection strategy of insulation system for marine current turbine , 2019, Mechanical Systems and Signal Processing.
[11] Mahdi Mahfouf,et al. Support Vector Machines for Class Imbalance Rail Data Classification with Bootstrapping-based Over-Sampling and Under-Sampling , 2014 .
[12] Daniel S. Yeung,et al. Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems , 2015, IEEE Transactions on Cybernetics.
[13] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[14] Ruyi Huang,et al. Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis , 2019, IEEE Access.
[15] Yan Liu,et al. Granger Causality for Time-Series Anomaly Detection , 2012, 2012 IEEE 12th International Conference on Data Mining.
[16] Liang Guo,et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.
[17] Zhang Yi,et al. Audio classification using attention-augmented convolutional neural network , 2018, Knowl. Based Syst..
[18] Yaguo Lei,et al. A Deep Learning-based Method for Machinery Health Monitoring with Big Data , 2015 .
[19] Chen Zhiguo. A data-driven fault propagation analysis method , 2010 .
[20] Liu Yang,et al. A Classification Method for Class-Imbalanced Data and Its Application on Bioinformatics , 2010 .
[21] Ming-Yu Liu,et al. Coupled Generative Adversarial Networks , 2016, NIPS.
[22] Francisco Herrera,et al. Imbalance: Oversampling algorithms for imbalanced classification in R , 2018, Knowl. Based Syst..
[23] Jun Wang,et al. An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition , 2018, Neurocomputing.
[24] Yunchao Wei,et al. Perceptual Generative Adversarial Networks for Small Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Zhe Wang,et al. IMCStacking: Cost-sensitive stacking learning with feature inverse mapping for imbalanced problems , 2018, Knowl. Based Syst..
[26] Teng Li,et al. Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder , 2017 .
[27] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[28] Chao Liu,et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults , 2019, Knowl. Based Syst..
[29] Shi Li,et al. A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals , 2019, Comput. Ind..
[30] Pingfeng Wang,et al. Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..
[31] Xin Zhou,et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .
[32] Shuilong He,et al. A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection , 2017, Knowl. Based Syst..
[33] Hongyuan Zha,et al. Entropy-based fuzzy support vector machine for imbalanced datasets , 2017, Knowl. Based Syst..
[34] Zhoujun Li,et al. From content to links: Social image embedding with deep multimodal model , 2018, Knowl. Based Syst..
[35] Hamido Fujita,et al. Multi-Imbalance: An open-source software for multi-class imbalance learning , 2019, Knowl. Based Syst..
[36] Yingwei Zhang,et al. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM , 2009 .
[37] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[38] Xindong Wu,et al. Online feature selection for high-dimensional class-imbalanced data , 2017, Knowl. Based Syst..
[39] Yue Xu,et al. Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets , 2018, Inf. Sci..
[40] 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 .
[41] Hao Wu,et al. An adaptive confidence limit for periodic non-steady conditions fault detection , 2016 .
[42] José Valente de Oliveira,et al. A Bayesian approach to consequent parameter estimation in probabilistic fuzzy systems and its application to bearing fault classification , 2017, Knowl. Based Syst..