Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification
暂无分享,去创建一个
Chen Lu | Bo Zhou | Zhenya Wang | Chen Lu | Zhenya Wang | Bo Zhou
[1] Lianwen Jin,et al. DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition , 2015, Pattern Recognit..
[2] Neculai Andrei. An adaptive conjugate gradient algorithm for large-scale unconstrained optimization , 2016, J. Comput. Appl. Math..
[3] Steven B. Damelin,et al. The Mathematics of Signal Processing , 2012 .
[4] Chung-Ho Hsieh,et al. Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. , 2011, Surgery.
[5] Masoud Masoumi,et al. Using continuous wavelet transform of generalized flexibility matrix in damage identification , 2013 .
[6] Thomas Hofmann,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2007 .
[7] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[8] Te Han,et al. The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value , 2016 .
[9] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[10] Robert B. Randall,et al. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .
[11] Yuanyuan Zhang,et al. Adaptive Convolutional Neural Network and Its Application in Face Recognition , 2016, Neural Processing Letters.
[12] Jinde Zheng,et al. Rolling bearing fault diagnosis based on partially ensemble empirical mode decomposition and variable predictive model-based class discrimination , 2016 .
[13] Quoc V. Le,et al. Recurrent Neural Networks for Noise Reduction in Robust ASR , 2012, INTERSPEECH.
[14] Bo Du,et al. Scene Classification via a Gradient Boosting Random Convolutional Network Framework , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[15] Stéphane Mallat,et al. Understanding deep convolutional networks , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[16] Jing Yuan,et al. Multiwavelet transform and its applications in mechanical fault diagnosis – A review , 2014 .
[17] Fulei Chu,et al. Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples , 2013 .
[18] Hubert Cecotti,et al. Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Jay Lee,et al. Novel method for rolling element bearing health assessment—A tachometer-less synchronously averaged envelope feature extraction technique , 2012 .
[20] P. Flandrin,et al. Empirical Mode Decomposition , 2012 .
[21] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[22] Diego Cabrera,et al. Fault diagnosis in spur gears based on genetic algorithm and random forest , 2016 .
[23] Mustafa Demetgul,et al. Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network , 2014 .
[24] Jian Ma,et al. Health assessment and fault diagnosis for centrifugal pumps using Softmax regression , 2014 .
[25] Joseph Mathew,et al. Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .
[26] Thomas Hofmann,et al. Greedy Layer-Wise Training of Deep Networks , 2007 .
[27] Ah Chung Tsoi,et al. Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.
[28] Osonde Osoba,et al. Noise-enhanced convolutional neural networks , 2016, Neural Networks.
[29] Jay Lee,et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .
[30] Jay Lee,et al. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.
[31] Mihai Gavrilescu. Improved automatic speech recognition system using sparse decomposition by basis pursuit with deep rectifier neural networks and compressed sensing recomposition of speech signals , 2014, 2014 10th International Conference on Communications (COMM).
[32] Dejie Yu,et al. A new rolling bearing fault diagnosis method based on GFT impulse component extraction , 2016 .
[33] Wenxian Yang,et al. Empirical mode decomposition, an adaptive approach for interpreting shaft vibratory signals of large rotating machinery , 2009 .
[34] Yu Yang,et al. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .
[35] Jürgen Schmidhuber,et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.
[36] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[37] Jay Lee,et al. Enhanced diagnostic certainty using information entropy theory , 2003, Adv. Eng. Informatics.
[38] Cécile Barat,et al. String representations and distances in deep Convolutional Neural Networks for image classification , 2016, Pattern Recognit..
[39] David A. Forsyth,et al. Shape, Contour and Grouping in Computer Vision , 1999, Lecture Notes in Computer Science.
[40] Jian Xiao,et al. Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings , 2014, Appl. Math. Comput..
[41] Yi Wang,et al. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network , 2013, J. Intell. Manuf..
[42] Ashkan Moosavian,et al. 814. Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbor and support vector machine , 2012 .
[43] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[44] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[45] V. Sugumaran,et al. Fault diagnosis of monoblock centrifugal pump using SVM , 2014 .
[46] Christian Viard-Gaudin,et al. A Convolutional Neural Network Approach for Objective Video Quality Assessment , 2006, IEEE Transactions on Neural Networks.
[47] Pingfeng Wang,et al. Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..
[48] Wenyi Zhang,et al. A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA , 2015, Adv. Eng. Informatics.
[49] Liangsheng Qu,et al. Diagnosis of subharmonic faults of large rotating machinery based on EMD , 2009 .
[50] Amparo Alonso-Betanzos,et al. Automatic bearing fault diagnosis based on one-class ν-SVM , 2013, Comput. Ind. Eng..