VMD-Based Ensembled SMOTEBoost for Imbalanced Multi-class Rotor Mass Imbalance Fault Detection and Diagnosis Under Industrial Noise

[1]  Ruqiang Yan,et al.  Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis , 2021, Reliab. Eng. Syst. Saf..

[2]  Jingyue Wang,et al.  Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition , 2020 .

[3]  Mohammad Gohari,et al.  Modelling of shaft unbalance: Modelling a multi discs rotor using K-Nearest Neighbor and Decision Tree Algorithms , 2020 .

[4]  C. Faloutsos,et al.  Ensemble Methods , 2019, Machine Learning with Spark™ and Python®.

[5]  Gino Iannace,et al.  Fault Diagnosis for UAV Blades Using Artificial Neural Network , 2019, Robotics.

[6]  Amit Kumar Tyagi,et al.  A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review , 2019, Journal of Computer Science.

[7]  Wei Wu,et al.  A high-speed D-CART online fault diagnosis algorithm for rotor systems , 2019, Applied Intelligence.

[8]  Cesar da Costa,et al.  Vibration Analysis in Turbomachines Using Machine Learning Techniques , 2019, European Journal of Engineering Research and Science.

[9]  Hae-Jin Choi,et al.  Fault Diagnosis of Planetary Gear Carrier Packs: A Class Imbalance and Multiclass Classification Problem , 2019, International Journal of Precision Engineering and Manufacturing.

[10]  Jay Lee,et al.  Industrial Artificial Intelligence for industry 4.0-based manufacturing systems , 2018, Manufacturing Letters.

[11]  Mehdi Behzad,et al.  Unbalance-induced rub between rotor and compliant-segmented stator , 2018, Journal of Sound and Vibration.

[12]  Hasmat Malik,et al.  Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and Simulink , 2017 .

[13]  Lixiang Duan,et al.  A new support vector data description method for machinery fault diagnosis with unbalanced datasets , 2016, Expert Syst. Appl..

[14]  Elineudo Pinho de Moura,et al.  Classification of imbalance levels in a scaled wind turbine through detrended fluctuation analysis of vibration signals , 2016 .

[15]  Luís Torgo,et al.  A Survey of Predictive Modeling on Imbalanced Domains , 2016, ACM Comput. Surv..

[16]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[17]  Allan J. Volponi,et al.  Gas Turbine Engine Health Management: Past, Present, and Future Trends , 2014 .

[18]  Ian K. Jennions,et al.  Unbalance localization through machine nonlinearities using an artificial neural network approach , 2014 .

[19]  Fabrice Thouverez,et al.  Rotor to stator contacts in turbomachines. Review and application , 2013 .

[20]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[21]  Scott T. Rickard,et al.  A formal study of the nonlinearity and consistency of the Empirical Mode Decomposition , 2012, Signal Process..

[22]  Yi Shen,et al.  Boundary extension for Hilbert-Huang transform inspired by gray prediction model , 2012, Signal Process..

[23]  A. S. Sekhar,et al.  Identification of unbalance in a rotor bearing system , 2011 .

[24]  Michael Feldman,et al.  Analytical basics of the EMD: Two harmonics decomposition , 2009 .

[25]  Tejas H. Patel,et al.  Experimental investigations on vibration response of misaligned rotors , 2009 .

[26]  Li Lin,et al.  Signal feature extraction based on an improved EMD method , 2009 .

[27]  Shaoze Yan,et al.  A revised Hilbert–Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure , 2008 .

[28]  Zhijing Yang,et al.  A method to eliminate riding waves appearing in the empirical AM/FM demodulation , 2008, Digit. Signal Process..

[29]  A. Bouzidane,et al.  An electrorheological hydrostatic journal bearing for controlling rotor vibration , 2008 .

[30]  G. Kerschen,et al.  Toward a Fundamental Understanding of the Hilbert-Huang Transform in Nonlinear Structural Dynamics , 2006 .

[31]  Tsuyoshi Inoue,et al.  Detection of a Rotor Crack Using a Harmonic Excitation and Nonlinear Vibration Analysis , 2006 .

[32]  J. Sinou,et al.  The influence of crack-imbalance orientation and orbital evolution for an extended cracked Jeffcott rotor , 2004, 0801.3019.

[33]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[34]  A. S. Sekhar,et al.  VIBRATION CHARACTERISTICS OF A CRACKED ROTOR WITH TWO OPEN CRACKS , 1999 .

[35]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[36]  James T. Reason,et al.  Managing the risks of organizational accidents , 1997 .

[37]  K. Esbensen,et al.  Principal component analysis , 1987 .

[38]  Santi Wulan Purnami,et al.  Boosting Support Vector Machines for Imbalanced Microarray Data , 2018, INNS Conference on Big Data.

[39]  Lei Lu,et al.  Shaft Orbit Feature Based Rotator Early Unbalance Fault Identification , 2016 .

[40]  Longbo Chen,et al.  FINDING STRUCTURE WITH RANDOMNESS : PROBABILISTIC ALGORITHMS FOR CONSTRUCTING , 2016 .

[41]  Cha Zhang,et al.  Ensemble Machine Learning , 2012 .

[42]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[43]  Jörg Wauer,et al.  On the Dynamics of Cracked Rotors: A Literature Survey , 1990 .