A new Wasserstein distance- and cumulative sum-dependent health indicator and its application in prediction of remaining useful life of bearing
暂无分享,去创建一个
[1] E. S. Page. CONTINUOUS INSPECTION SCHEMES , 1954 .
[2] R. M. Stewart,et al. Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis , 1978 .
[3] David Mumford,et al. Mathematical theories of shape: do they model perception? , 1991, Optics & Photonics.
[4] Peter J. Bickel,et al. The Earth Mover's distance is the Mallows distance: some insights from statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[5] Nagi Gebraeel,et al. Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.
[6] Günter Rote,et al. Matching point sets with respect to the Earth mover's distance , 2005, EuroCG.
[7] K. Goebel,et al. Prognostic information fusion for constant load systems , 2005, 2005 7th International Conference on Information Fusion.
[8] Hai Qiu,et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .
[9] Alaa Elwany,et al. Sensor-driven prognostic models for equipment replacement and spare parts inventory , 2008 .
[10] K. Goebel,et al. Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.
[11] Zheng Bao,et al. Variational Color Image Segmentation via Chromaticity-Brightness Decomposition , 2010, MMM.
[12] Chao Hu,et al. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life , 2011, 2011 IEEE Conference on Prognostics and Health Management.
[13] Robert X. Gao,et al. Prognosis of Defect Propagation Based on Recurrent Neural Networks , 2011, IEEE Transactions on Instrumentation and Measurement.
[14] P. Maravelakis,et al. A CUSUM control chart for monitoring the variance when parameters are estimated , 2011 .
[15] Brigitte Chebel-Morello,et al. PRONOSTIA : An experimental platform for bearings accelerated degradation tests. , 2012 .
[16] Theodoros H. Loutas,et al. Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression , 2013, IEEE Transactions on Reliability.
[17] Gang Yu,et al. A new statistical modeling and detection method for rolling element bearing faults based on alpha–stable distribution , 2013 .
[18] Noureddine Zerhouni,et al. Data-driven prognostics based on health indicator construction: Application to PRONOSTIA's data , 2013, 2013 European Control Conference (ECC).
[19] Ruqiang Yan,et al. Bearing Degradation Evaluation Using Recurrence Quantification Analysis and Kalman Filter , 2014, IEEE Transactions on Instrumentation and Measurement.
[20] E. Jantunen,et al. A descriptive model of wear evolution in rolling bearings , 2014 .
[21] M. Lewis,et al. A generalized residual technique for analysing complex movement models using earth mover's distance , 2014, 1402.1805.
[22] Christoph Schnörr,et al. Globally Optimal Joint Image Segmentation and Shape Matching Based on Wasserstein Modes , 2014, Journal of Mathematical Imaging and Vision.
[23] Selin Aviyente,et al. Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings , 2015, IEEE Transactions on Industrial Electronics.
[24] Youxian Sun,et al. Remaining Useful Life Prediction for a Nonlinear Heterogeneous Wiener Process Model With an Adaptive Drift , 2015, IEEE Transactions on Reliability.
[25] Brigitte Chebel-Morello,et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network , 2015 .
[26] Yaguo Lei,et al. An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.
[27] Brigitte Chebel-Morello,et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .
[28] Noureddine Zerhouni,et al. Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics , 2015, IEEE Transactions on Industrial Electronics.
[29] Myeongsu Kang,et al. A Massively Parallel Approach to Real-Time Bearing Fault Detection Using Sub-Band Analysis on an FPGA-Based Multicore System , 2016, IEEE Transactions on Industrial Electronics.
[30] Yaguo Lei,et al. A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction , 2016, IEEE Transactions on Instrumentation and Measurement.
[31] Yaguo Lei,et al. A Model-Based Method for Remaining Useful Life Prediction of Machinery , 2016, IEEE Transactions on Reliability.
[32] Bin Zhang,et al. Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings , 2016, Qual. Reliab. Eng. Int..
[33] Bing Wang,et al. The application of a general mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing , 2017 .
[34] Wenhai Wang,et al. Remaining useful life prediction for an adaptive skew-Wiener process model , 2017 .
[35] Hesam Addin Arghand,et al. Estimation of Remaining Useful Life of Rolling Element Bearings Using Wavelet Packet Decomposition and Artificial Neural Network , 2018, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.
[36] Xuanqin Mou,et al. Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss , 2017, IEEE Transactions on Medical Imaging.
[37] Lin Li,et al. Route Planning Based on Genetic Algorithm , 2018 .
[38] A. Haq,et al. Improved CUSUM charts for monitoring process mean , 2018 .
[39] Jong-Myon Kim,et al. A Hybrid Prognostics Technique for Rolling Element Bearings Using Adaptive Predictive Models , 2018, IEEE Transactions on Industrial Electronics.
[40] Equivalence Testing of Complex Particle Size Distribution Profiles Based on Earth Mover’s Distance , 2018, The AAPS Journal.
[41] Yaguo Lei,et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .
[42] Lei Ren,et al. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks , 2018, Journal of Manufacturing Systems.
[43] Jong-Myon Kim,et al. A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models , 2018, Reliab. Eng. Syst. Saf..
[44] Weiwen Peng,et al. Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network , 2019, IEEE Transactions on Industrial Electronics.
[45] H. Kirkegaard,et al. Crowding in the emergency department in the absence of boarding – a transition regression model to predict departures and waiting time , 2019, BMC Medical Research Methodology.