Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery
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[1] Yongbo Li,et al. Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform , 2017 .
[2] Mohamed Elforjani,et al. Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning , 2018, IEEE Transactions on Industrial Electronics.
[3] Laming Chen,et al. Square-Root Lasso With Nonconvex Regularization: An ADMM Approach , 2016, IEEE Signal Processing Letters.
[4] Wei Li,et al. Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator , 2017 .
[5] Qing Li,et al. Degradation Trend Prognostics for Rolling Bearing Using Improved R/S Statistic Model and Fractional Brownian Motion Approach , 2018, IEEE Access.
[6] Yaguo Lei,et al. An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.
[7] Ming Li,et al. Prediction of Bearing Fault Using Fractional Brownian Motion and Minimum Entropy Deconvolution , 2016, Entropy.
[8] Qingbo He,et al. Sparse representation based on local time–frequency template matching for bearing transient fault feature extraction , 2016 .
[9] Sophia Daskalaki,et al. Comparing forecasting approaches for Internet traffic , 2015, Expert Syst. Appl..
[10] Yaguo Lei,et al. A Model-Based Method for Remaining Useful Life Prediction of Machinery , 2016, IEEE Transactions on Reliability.
[11] Alexander Jung,et al. Graphical LASSO based Model Selection for Time Series , 2014, IEEE Signal Processing Letters.
[12] Tsung-Hui Chang,et al. A Proximal Dual Consensus ADMM Method for Multi-Agent Constrained Optimization , 2014, IEEE Transactions on Signal Processing.
[13] Qin Hu,et al. Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator , 2017 .
[14] Zhichao Dong,et al. Robust linear equation dwell time model compatible with large scale discrete surface error matrix. , 2015, Applied optics.
[15] Liang Guo,et al. Remaining Useful Life Prediction Based on a General Expression of Stochastic Process Models , 2017, IEEE Transactions on Industrial Electronics.
[16] Ivo Paixao de Medeiros,et al. Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering , 2018, Reliab. Eng. Syst. Saf..
[17] 松本 隆,et al. Deconvolution , 1997, Computer Vision, A Reference Guide.
[18] Shuilong He,et al. Compressed sparse time–frequency feature representation via compressive sensing and its applications in fault diagnosis , 2015 .
[19] Masoud Hajarian. Extending LSQR methods to solve the generalized Sylvester-transpose and periodic Sylvester matrix equations , 2014 .
[20] Mohammad Karimi,et al. Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems. , 2017, ISA transactions.
[21] Roberto Sassi,et al. Low Computational Cost for Sample Entropy , 2018, Entropy.
[22] Liu Yu,et al. Improved R/S Algorithm Based on Network Traffic Self-Similarity , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.
[23] Huihui Miao,et al. Feature Identification With Compressive Measurements for Machine Fault Diagnosis , 2015, IEEE Transactions on Instrumentation and Measurement.
[24] Ming Li,et al. Sparse Reconstruction Based on the ADMM and Lasso-LSQR for Bearings Vibration Signals , 2017, IEEE Access.
[25] Xiang Li,et al. Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..
[26] N. Sneeuw,et al. Comparison of methods for a 3-D density inversion from airborne gravity gradiometry , 2017, Studia Geophysica et Geodaetica.
[27] Brigitte Chebel-Morello,et al. PRONOSTIA : An experimental platform for bearings accelerated degradation tests. , 2012 .
[28] Sophia Daskalaki,et al. Dynamic Bandwidth Allocation for Video Traffic Using FARIMA-Based Forecasting Models , 2018, Journal of Network and Systems Management.
[29] Enrico Zio,et al. An adaptive method for health trend prediction of rotating bearings , 2014, Digit. Signal Process..
[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] Stephen P. Boyd,et al. An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems , 2012, 1203.1828.