A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning
暂无分享,去创建一个
[1] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[2] Peter W. Tse,et al. Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .
[3] V. Sugumaran,et al. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines , 2015 .
[4] Xiang Li,et al. Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..
[5] Claude Delpha,et al. An optimal fault detection threshold for early detection using Kullback-Leibler Divergence for unknown distribution data , 2016, Signal Process..
[6] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.
[7] Zhang Yi,et al. A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data , 2013, IEEE Transactions on Neural Networks and Learning Systems.
[8] Hu Xiao,et al. Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance , 2015 .
[9] L. Qiang,et al. Engineering signal processing based on adaptive step-changed stochastic resonance , 2007 .
[10] Khe Chai Sim,et al. On combining i-vectors and discriminative adaptation methods for unsupervised speaker normalization in DNN acoustic models , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[11] Moncef Gabbouj,et al. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.
[12] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[13] Bingyuan Liu,et al. Regularized Hierarchical Feature Learning with Non-negative Sparsity and Selectivity for Image Classification , 2014, 2014 22nd International Conference on Pattern Recognition.
[14] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[15] Moncef Gabbouj,et al. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks , 2017 .
[16] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Tao Zhang,et al. Deep Model Based Domain Adaptation for Fault Diagnosis , 2017, IEEE Transactions on Industrial Electronics.
[18] Yuan Li,et al. Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study , 2017 .
[19] Guolin He,et al. Semisupervised Distance-Preserving Self-Organizing Map for Machine-Defect Detection and Classification , 2013, IEEE Transactions on Instrumentation and Measurement.
[20] Sivaraman Balakrishnan,et al. Optimal kernel choice for large-scale two-sample tests , 2012, NIPS.
[21] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[22] 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.
[23] Amir Globerson,et al. Metric Learning by Collapsing Classes , 2005, NIPS.
[24] Wenliao Du,et al. Wavelet leaders multifractal features based fault diagnosis of rotating mechanism , 2014 .
[25] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[26] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[27] Siliang Lu,et al. Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals , 2016 .
[28] Jing Yuan,et al. Multiwavelet transform and its applications in mechanical fault diagnosis – A review , 2014 .
[29] Fengshou Gu,et al. Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis , 2013 .
[30] Lixiang Duan,et al. On cross-domain feature fusion in gearbox fault diagnosis under various operating conditions based on Transfer Component Analysis , 2016, 2016 IEEE International Conference on Prognostics and Health Management (ICPHM).
[31] Taiyong Wang,et al. Study on non-linear filter characteristic and engineering application of cascaded bistable stochastic resonance system , 2007 .
[32] Ivan Prebil,et al. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis , 2016 .
[33] Wei Zhang,et al. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals , 2017, Sensors.
[34] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[35] Tinne Tuytelaars,et al. Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.
[36] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[37] Chen Lu,et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..
[38] Tinne Tuytelaars,et al. Lightweight Unsupervised Domain Adaptation by Convolutional Filter Reconstruction , 2016, ECCV Workshops.
[39] Yongbin Liu,et al. Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis , 2017 .
[40] Inderjit S. Dhillon,et al. Information-theoretic metric learning , 2006, ICML '07.
[41] Kilian Q. Weinberger,et al. Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.
[42] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[43] Bin Li,et al. Crack fault diagnosis of rotor systems using wavelet transforms , 2015, Comput. Electr. Eng..
[44] Horst Bischof,et al. Large scale metric learning from equivalence constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[45] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[46] Jiwen Lu,et al. Automatic Subspace Learning via Principal Coefficients Embedding , 2014, IEEE Transactions on Cybernetics.
[47] Huibin Lin,et al. Fault feature extraction of rolling element bearings using sparse representation , 2016 .
[48] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[49] Tara N. Sainath,et al. Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[50] Balbir S. Dhillon,et al. Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network , 2012 .
[51] Tommy W. S. Chow,et al. Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis , 2014, IEEE Transactions on Industrial Electronics.
[52] Zhang Yi,et al. Connections Between Nuclear-Norm and Frobenius-Norm-Based Representations , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[53] Feng Jia,et al. An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.
[54] Ruqiang Yan,et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .
[55] Chandrasekhar Nataraj,et al. Use of particle swarm optimization for machinery fault detection , 2009, Eng. Appl. Artif. Intell..
[56] Ruqiang Yan,et al. Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.
[57] Richard S. Zemel,et al. Generative Moment Matching Networks , 2015, ICML.
[58] Bo-Suk Yang,et al. Random forests classifier for machine fault diagnosis , 2008 .
[59] Yitao Liang,et al. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM , 2015 .
[60] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[61] Dong Xu,et al. Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[62] Du Tran,et al. Human Activity Recognition with Metric Learning , 2008, ECCV.
[63] Liang Chen,et al. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis , 2016 .
[64] Jing Liu,et al. Robust Structured Subspace Learning for Data Representation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[65] Wei Chen,et al. Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization , 2015, Neurocomputing.
[66] Barend J. van Wyk,et al. Difference Histograms: A new tool for time series analysis applied to bearing fault diagnosis , 2009, Pattern Recognit. Lett..
[67] Jeff G. Schneider,et al. Flexible Transfer Learning under Support and Model Shift , 2014, NIPS.
[68] D. Jacobs,et al. Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch , 2011, CVPR 2011.
[69] Zhang Yi,et al. Scalable Sparse Subspace Clustering , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[70] Huajing Fang,et al. Diversified learning for continuous hidden Markov models with application to fault diagnosis , 2015, Expert Syst. Appl..
[71] Robert B. Randall,et al. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .
[72] S.A.V. Satya Murty,et al. Roller element bearing fault diagnosis using singular spectrum analysis , 2013 .
[73] Zhang Yi,et al. Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering , 2012, IEEE Transactions on Cybernetics.