Phase Space Similarity as a Signature for Rolling Bearing Fault Diagnosis and Remaining Useful Life Estimation
暂无分享,去创建一个
Bing He | Fang Liu | Siliang Lu | Jiwen Zhao | Yongbin Liu | Yilei Zhao | Yongbin Liu | Siliang Lu | Jiwen Zhao | Fang Liu | Yilei Zhao | Bing He
[1] Hai Qiu,et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .
[2] Zhi Gao Luo,et al. Fault classification of rolling bearing based on reconstructed phase space and Gaussian mixture model , 2009 .
[3] M. Rosenstein,et al. A practical method for calculating largest Lyapunov exponents from small data sets , 1993 .
[4] L. Cao. Practical method for determining the minimum embedding dimension of a scalar time series , 1997 .
[5] F. Takens. Detecting strange attractors in turbulence , 1981 .
[6] Andrés Bustillo,et al. An SVM-Based Solution for Fault Detection in Wind Turbines , 2015, Sensors.
[7] Qingbo He,et al. Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis , 2013, Sensors.
[8] Du-Ming Tsai,et al. Fast normalized cross correlation for defect detection , 2003, Pattern Recognit. Lett..
[9] Zhenhai Guo,et al. A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm , 2014 .
[10] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[11] Qingbo He,et al. Time–frequency manifold for nonlinear feature extraction in machinery fault diagnosis , 2013 .
[12] Hugo Jiménez-Hernández,et al. Background Subtraction Approach based on Independent Component Analysis , 2010, Sensors.
[13] Qingbo He,et al. A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification , 2015 .
[14] Xiang Wang,et al. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding , 2015, Sensors.
[15] Ligang Cai,et al. Adaptive Redundant Lifting Wavelet Transform Based on Fitting for Fault Feature Extraction of Roller Bearings , 2012, Sensors.
[16] Shaojiang Dong,et al. Bearing degradation process prediction based on the PCA and optimized LS-SVM model , 2013 .
[17] Fanrang Kong,et al. Rolling Bearing Localized Defect Evaluation by Multiscale Signature via Empirical Mode Decomposition , 2012 .
[18] Ramli Nazir,et al. Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN , 2014 .
[19] Ying Zhang,et al. Classification of fault location and performance degradation of a roller bearing , 2013 .
[20] Qing Zhang,et al. Remaining useful life estimation for mechanical systems based on similarity of phase space trajectory , 2015, Expert Syst. Appl..
[21] Christopher E. Hann,et al. Fast normalized cross correlation for motion tracking using basis functions , 2006, Comput. Methods Programs Biomed..
[22] Changqing Shen,et al. Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines , 2014 .
[23] Hui Jiang,et al. Grey relational grade in local support vector regression for financial time series prediction , 2012, Expert Syst. Appl..
[24] B. Samanta,et al. ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .
[25] Lisa M. Brown,et al. A survey of image registration techniques , 1992, CSUR.
[26] Luigi di Stefano,et al. An efficient algorithm for exhaustive template matching based on normalized cross correlation , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..
[27] Zheng Yao,et al. Wind power forecasting based on principle component phase space reconstruction , 2015 .
[28] Sun Ya-ming. A NEW MODEL OF STLF BASED ON THE FUSION OF PSRT AND CHAOTIC NEURAL NETWORKS , 2004 .
[29] Yuanyuan Pan,et al. Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections , 2014 .
[30] James P. Crutchfield,et al. Geometry from a Time Series , 1980 .