Random forest-based nonlinear improved feature extraction and selection for fault classification

In this paper, Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) proposed for fault detection and diagnosis (FDD) due to its effectiveness in handling uncertain industrial process data, which are often with high nonlinearities and strong correlations. This technique aims to extract the features from raw data using IGPR technique. Then, the interval mean vector and the interval variance matrix obtained from IGPR technique are used as inputs to the Random Forest (RF) classifier. The results show the effectiveness of the features and the classifiers in detection of faults of Wind Energy Conversion (WEC) Systems.

[1]  Mohamed N. Nounou,et al.  A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems , 2020, IEEE Access.

[2]  Sayanjit Singha Roy,et al.  Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework , 2020, IEEE Sensors Journal.

[3]  Diego Cabrera,et al.  Fault diagnosis in spur gears based on genetic algorithm and random forest , 2016 .

[4]  Binh Thai Pham,et al.  Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers , 2020, Geocarto International.

[5]  Peng Kou,et al.  Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning , 2015 .

[6]  Nadeem Javaid,et al.  Fault Detection in Wireless Sensor Networks through the Random Forest Classifier , 2019, Sensors.

[7]  Sergio L. Netto,et al.  Fault detection and classification in oil wells and production/service lines using random forest , 2020 .

[8]  Mohamed Faouzi Harkat,et al.  Fault detection of uncertain nonlinear process using interval-valued data-driven approach , 2020, 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD).

[9]  Hossam Faris,et al.  An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio , 2019, International Journal of Machine Learning and Cybernetics.

[10]  Mohsen Rahimi,et al.  Improvement of energy conversion efficiency and damping of wind turbine response in grid connected DFIG based wind turbines , 2018 .

[11]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[12]  Hazem Nounou,et al.  Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems , 2020 .

[13]  Shan Chen,et al.  Model Calibration Method for Soft Sensors Using Adaptive Gaussian Process Regression , 2019, IEEE Access.

[14]  Jie Yu,et al.  Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach , 2012 .

[15]  Chunhui Zhao,et al.  Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification , 2020, IEEE Transactions on Industrial Informatics.

[16]  Liangxiao Jiang,et al.  Class-specific attribute weighted naive Bayes , 2019, Pattern Recognit..

[17]  Haibo He,et al.  Self-adaptive manifold discriminant analysis for feature extraction from hyperspectral imagery , 2020, Pattern Recognit..

[18]  Birendra Biswal,et al.  Robust classification of neovascularization using random forest classifier via convoluted vascular network , 2021, Biomed. Signal Process. Control..

[19]  V. K. Giri,et al.  Feature selection and classification of mechanical fault of an induction motor using random forest classifier , 2016 .

[20]  Stig Munk-Nielsen,et al.  Lifetime investigation of high power IGBT modules , 2011, Proceedings of the 2011 14th European Conference on Power Electronics and Applications.

[21]  Jian Xiao,et al.  Short-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression , 2018, IEEE Transactions on Industrial Electronics.

[22]  Mingxin Zhao,et al.  Series Arc Detection and Complex Load Recognition Based on Principal Component Analysis and Support Vector Machine , 2019, IEEE Access.