Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis
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[1] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[2] Maryam Farajzadeh-Zanjani,et al. Efficient feature extraction of vibration signals for diagnosing bearing defects in induction motors , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[3] Baoping Tang,et al. Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier , 2014 .
[4] N. Tandon,et al. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .
[5] Qingbo He,et al. A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification , 2015 .
[6] David Zhang,et al. A method for speeding up feature extraction based on KPCA , 2007, Neurocomputing.
[7] Baoping Tang,et al. A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm , 2013 .
[8] Jianbo Yu,et al. Local and global principal component analysis for process monitoring , 2012 .
[9] Yi Qin,et al. Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine , 2015, Neurocomputing.
[10] Shuicheng Yan,et al. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007 .
[11] Ming Liang,et al. Fault severity assessment for rolling element bearings using the Lempel–Ziv complexity and continuous wavelet transform , 2009 .
[12] Feng Lu,et al. Recursive reduced kernel based extreme learning machine for aero-engine fault pattern recognition , 2016, Neurocomputing.
[13] Zhengming Ma,et al. Local Coordinates Alignment With Global Preservation for Dimensionality Reduction , 2013, IEEE Transactions on Neural Networks and Learning Systems.
[14] Maryam Farajzadeh-Zanjani,et al. An Integrated Class-Imbalanced Learning Scheme for Diagnosing Bearing Defects in Induction Motors , 2017, IEEE Transactions on Industrial Informatics.
[15] Yaguo Lei,et al. Gear crack level identification based on weighted K nearest neighbor classification algorithm , 2009 .
[16] Robert B. Randall,et al. Rolling element bearing diagnostics—A tutorial , 2011 .
[17] Satish C. Sharma,et al. Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform , 2013, Neurocomputing.
[18] Maryam Farajzadeh-Zanjani,et al. Dimensionality reduction-based diagnosis of bearing defects in induction motors , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[19] Roozbeh Razavi-Far,et al. zSlices-Based General Type-2 Fuzzy Fusion of Support Vector Machines With Application to Bearing Fault Detection , 2017, IEEE Transactions on Industrial Electronics.
[20] Jiaxu Wang,et al. Weak fault diagnosis of rotating machinery based on feature reduction with Supervised Orthogonal Local Fisher Discriminant Analysis , 2015, Neurocomputing.
[21] Yurong Liu,et al. A survey of deep neural network architectures and their applications , 2017, Neurocomputing.
[22] Paul Geladi,et al. Principal Component Analysis , 1987, Comprehensive Chemometrics.
[23] Enrico Zio,et al. Efficient residuals pre-processing for diagnosing multi-class faults in a doubly fed induction generator, under missing data scenarios , 2014, Expert Syst. Appl..
[24] Yu Zhang,et al. Vibration response mechanism of faulty outer race rolling element bearings for quantitative analysis , 2016 .
[25] Xiaofei He,et al. Locality Preserving Projections , 2003, NIPS.
[26] Enrico Zio,et al. Adaptive incremental ensemble of extreme learning machines for fault diagnosis in induction motors , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[27] Amir Globerson,et al. Metric Learning by Collapsing Classes , 2005, NIPS.
[28] Jafar Zarei,et al. Induction motors bearing fault detection using pattern recognition techniques , 2012, Expert Syst. Appl..
[29] Jing-Yu Yang,et al. Unsupervised discriminant canonical correlation analysis based on spectral clustering , 2016, Neurocomputing.
[30] Jiawei Han,et al. Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[31] Kuldip K. Paliwal,et al. A deterministic approach to regularized linear discriminant analysis , 2015, Neurocomputing.
[32] Lei Wang,et al. Multiple kernel extreme learning machine , 2015, Neurocomputing.
[33] Feng Jia,et al. An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.
[34] Joshua B. Tenenbaum,et al. Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.
[35] Zhi-huan Song,et al. Global–Local Structure Analysis Model and Its Application for Fault Detection and Identification , 2011 .
[36] Mohamed El Hachemi Benbouzid. A review of induction motors signature analysis as a medium for faults detection , 2000, IEEE Trans. Ind. Electron..
[37] Jay Lee,et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .
[38] Lingli Cui,et al. Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical–Horizontal Synchronization Signal Analysis , 2017, IEEE Transactions on Industrial Electronics.
[39] Honggang Zhang,et al. Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" , 2007, IEEE Trans. Pattern Anal. Mach. Intell..
[40] Yaguo Lei,et al. A multidimensional hybrid intelligent method for gear fault diagnosis , 2010, Expert Syst. Appl..
[41] Sahibsingh A. Dudani. The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.
[42] Roozbeh Razavi-Far,et al. Ensemble of extreme learning machines for diagnosing bearing defects in non-stationary environments under class imbalance condition , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).
[43] Xin Zhou,et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .
[44] Jianbo Yu,et al. Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment , 2012, IEEE Transactions on Industrial Electronics.
[45] Zhiwen Liu,et al. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings , 2013, Neurocomputing.
[46] Youxian Sun,et al. A novel fault diagnosis method based on optimal relevance vector machine , 2017, Neurocomputing.
[47] Liang Guo,et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.
[48] Qing Zhang,et al. A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method , 2018, Neurocomputing.