Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis
暂无分享,去创建一个
Mengjie Zhang | Shuting Wan | Bo Peng | Bing Xue | Ying Bi | Mengjie Zhang | Bing Xue | Ying Bi | Shuting Wan | Bo Peng
[1] Mengjie Zhang,et al. A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers Using Genetic Programming , 2012, IEEE Transactions on Evolutionary Computation.
[2] Xin Yao,et al. A Survey on Evolutionary Computation Approaches to Feature Selection , 2016, IEEE Transactions on Evolutionary Computation.
[3] Yuhao Wang,et al. High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder , 2019, IEEE Transactions on Industrial Electronics.
[4] Minqiang Xu,et al. A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection , 2017 .
[5] B. Pompe,et al. Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.
[6] Steven X. Ding,et al. A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.
[7] Moncef Gabbouj,et al. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.
[8] Minping Jia,et al. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing , 2018, Neurocomputing.
[9] Mengjie Zhang,et al. Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression , 2017, IEEE Transactions on Evolutionary Computation.
[10] Mengjie Zhang,et al. A domain independent Genetic Programming approach to automatic feature extraction for image classification , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).
[11] Thomas W. Rauber,et al. Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.
[12] Yaguo Lei,et al. Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .
[13] Norden E. Huang,et al. Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..
[14] Mengjie Zhang,et al. Two-Tier genetic programming: towards raw pixel-based image classification , 2012, Expert Syst. Appl..
[15] Haiyang Pan,et al. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines , 2017 .
[16] Mengjie Zhang,et al. A survey on evolutionary machine learning , 2019, Journal of the Royal Society of New Zealand.
[17] Hamed Azami,et al. Dispersion Entropy: A Measure for Time-Series Analysis , 2016, IEEE Signal Processing Letters.
[18] Dong Wang,et al. K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited , 2016 .
[19] Asoke K. Nandi,et al. Feature generation using genetic programming with application to fault classification , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[20] Guifa Teng,et al. A hybrid multiple feature construction approach for classification using Genetic Programming , 2019, Appl. Soft Comput..
[21] Jing Tian,et al. Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis , 2016, IEEE Transactions on Industrial Electronics.
[22] Dong Liu,et al. Feature extraction of rotor fault based on EEMD and curve code , 2019, Measurement.
[23] Ming J. Zuo,et al. Time-frequency representation based on robust local mean decomposition for multicomponent AM-FM signal analysis , 2017 .
[24] Mengjie Zhang,et al. An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming , 2018, EvoApplications.
[25] Minping Jia,et al. Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection , 2019, Knowl. Based Syst..
[26] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[27] Alessandro Paolo Daga,et al. The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data , 2019, Mechanical Systems and Signal Processing.
[28] Yanxue Wang,et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system , 2015 .
[29] 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.
[30] Marc Parizeau,et al. DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..
[31] Yongbo Li,et al. Fault Diagnosis of Rolling Bearing Under Speed Fluctuation Condition Based on Vold-Kalman Filter and RCMFE , 2018, IEEE Access.
[32] Mengjie Zhang,et al. Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification , 2020, IEEE Transactions on Cybernetics.
[33] Wangxin Yu,et al. Characterization of Surface EMG Signal Based on Fuzzy Entropy , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[34] Bing Xue,et al. An Effective Feature Learning Approach Using Genetic Programming With Image Descriptors for Image Classification [Research Frontier] , 2020, IEEE Computational Intelligence Magazine.
[35] Mark Johnston,et al. Low-Level Feature Extraction for Edge Detection Using Genetic Programming , 2014, IEEE Transactions on Cybernetics.
[36] Ming J. Zuo,et al. Joint amplitude and frequency demodulation analysis based on intrinsic time-scale decomposition for planetary gearbox fault diagnosis , 2016 .
[37] Jiong Tang,et al. Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning , 2017, IEEE Access.
[38] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[39] Dominique Zosso,et al. Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.
[40] Elsayed A. Elsayed,et al. On-Line Surveillance and Monitoring , 2000 .
[41] Evolutionary Constructive Induction , 2017, Encyclopedia of Machine Learning and Data Mining.
[42] J. Richman,et al. Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.
[43] John R. Koza,et al. Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.
[44] Saeid Sanei,et al. Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases , 2019, IEEE Access.
[45] Mengjie Zhang,et al. Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization , 2020, IEEE Transactions on Cybernetics.
[46] Jun Yan,et al. Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox , 2019, IEEE Transactions on Industrial Electronics.
[47] Nikhil R. Pal,et al. A Multiobjective Genetic Programming-Based Ensemble for Simultaneous Feature Selection and Classification , 2016, IEEE Transactions on Cybernetics.
[48] David J. Montana,et al. Strongly Typed Genetic Programming , 1995, Evolutionary Computation.
[49] Mengjie Zhang,et al. Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming , 2020, PPSN.
[50] Xiaohui Gu,et al. A novel Pareto-based Bayesian approach on extension of the infogram for extracting repetitive transients , 2018, Mechanical Systems and Signal Processing.
[51] Madalena Costa,et al. Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[52] Enrico Zio,et al. Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.
[53] Heng Li,et al. A time varying filter approach for empirical mode decomposition , 2017, Signal Process..
[54] Mengjie Zhang,et al. Genetic programming for multiple-feature construction on high-dimensional classification , 2019, Pattern Recognit..
[55] Qiang Miao,et al. An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis , 2018 .
[56] T. Narendiranath Babu,et al. Application of Butterworth filter for fault diagnosis on journal bearing , 2014 .
[57] Jinde Zheng,et al. A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy , 2013 .
[58] Yi Mei,et al. An Efficient Feature Selection Algorithm for Evolving Job Shop Scheduling Rules With Genetic Programming , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.
[59] Viliam Makis,et al. Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal , 2019, Mechanical Systems and Signal Processing.
[60] Leontios J. Hadjileontiadis,et al. Swarm decomposition: A novel signal analysis using swarm intelligence , 2017, Signal Process..
[61] Bong-Hwan Koh,et al. Wavelet energy-based visualization and classification of high-dimensional signal for bearing fault detection , 2014, Knowledge and Information Systems.
[62] 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.
[63] I. Osorio,et al. Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[64] Kay Chen Tan,et al. Deep Belief Networks Ensemble with Multi-objective Optimization for Failure Diagnosis , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.