Sparse Elitist Group Lasso Denoising in Frequency Domain for Bearing Fault Diagnosis

[1]  Alexander Hauptmann,et al.  Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Convolutional Neural Network , 2020, IEEE Transactions on Industrial Informatics.

[2]  Qiang Miao,et al.  Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis , 2018 .

[3]  Andrew Kusiak,et al.  Analyzing bearing faults in wind turbines: A data-mining approach , 2012 .

[4]  Jin Chen,et al.  Weak fault feature extraction of rolling bearings based on globally optimized sparse coding and approximate SVD , 2018, Mechanical Systems and Signal Processing.

[5]  Qing Li,et al.  An Improved Sparse Regularization Method for Weak Fault Diagnosis of Rotating Machinery Based Upon Acceleration Signals , 2018, IEEE Sensors Journal.

[6]  Zhibin Zhao,et al.  Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis , 2019, IEEE Transactions on Industrial Electronics.

[7]  Xiaobo Liu,et al.  Compound faults diagnosis based on customized balanced multiwavelets and adaptive maximum correlated kurtosis deconvolution , 2019, Measurement.

[8]  Shuangwen Sheng,et al.  Wind Turbine Gearbox Reliability Database, Condition Monitoring, and Operation and Maintenance Research Update , 2016 .

[9]  Adam Glowacz,et al.  Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals , 2019, Sensors.

[10]  Mohamed Benbouzid,et al.  The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines. , 2016, ISA transactions.

[11]  Xian-Bo Wang,et al.  Novel Particle Swarm Optimization-Based Variational Mode Decomposition Method for the Fault Diagnosis of Complex Rotating Machinery , 2017, IEEE/ASME Transactions on Mechatronics.

[12]  Genevera I. Allen,et al.  Within Group Variable Selection through the Exclusive Lasso , 2015, 1505.07517.

[13]  David He,et al.  Low speed bearing fault diagnosis using acoustic emission sensors , 2016 .

[14]  Xuefeng Chen,et al.  Fault Diagnosis for a Wind Turbine Generator Bearing via Sparse Representation and Shift-Invariant K-SVD , 2017, IEEE Transactions on Industrial Informatics.

[15]  Yongbo Li,et al.  Health Condition Monitoring and Early Fault Diagnosis of Bearings Using SDF and Intrinsic Characteristic-Scale Decomposition , 2016, IEEE Transactions on Instrumentation and Measurement.

[16]  Kai Siedenburg,et al.  Social Sparsity! Neighborhood Systems Enrich Structured Shrinkage Operators , 2013, IEEE Transactions on Signal Processing.

[17]  Ming Zeng,et al.  SOSO Boosting of the K-SVD Denoising Algorithm for Enhancing Fault-Induced Impulse Responses of Rolling Element Bearings , 2020, IEEE Transactions on Industrial Electronics.

[18]  Deirel Paz-Linares,et al.  Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models , 2017, Front. Neurosci..

[19]  Yi Qin,et al.  Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary , 2020, IEEE Transactions on Industrial Informatics.

[20]  Yaguo Lei,et al.  A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings , 2020, IEEE Transactions on Reliability.

[21]  J. Antoni,et al.  Fast computation of the spectral correlation , 2017 .

[22]  Ilker Bayram,et al.  A Penalty Function Promoting Sparsity Within and Across Groups , 2016, IEEE Transactions on Signal Processing.

[23]  Damien Garcia,et al.  Robust smoothing of gridded data in one and higher dimensions with missing values , 2010, Comput. Stat. Data Anal..

[24]  Huibin Lin,et al.  Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing impact fault diagnosis , 2018 .

[25]  Jocelyn Chanussot,et al.  Hyperspectral Image Unmixing With Endmember Bundles and Group Sparsity Inducing Mixed Norms , 2018, IEEE Transactions on Image Processing.

[26]  Jimeng Li,et al.  A novel adaptive stochastic resonance method based on coupled bistable systems and its application in rolling bearing fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[27]  Gaigai Cai,et al.  Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis , 2018, IEEE Transactions on Industrial Electronics.

[28]  Xiangyang Gong,et al.  Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity , 2016 .

[29]  Peng Chen,et al.  Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery , 2018, IEEE Transactions on Instrumentation and Measurement.

[30]  Xuefeng Chen,et al.  Gear fault diagnosis based on the structured sparsity time-frequency analysis , 2018 .

[31]  Xining Zhang,et al.  Periodical sparse low-rank matrix estimation algorithm for fault detection of rolling bearings. , 2020, ISA transactions.

[32]  Panagiotis Patrinos,et al.  Douglas-Rachford Splitting and ADMM for Nonconvex Optimization: Tight Convergence Results , 2017, SIAM J. Optim..

[33]  Yi Zhang,et al.  Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD. , 2019, ISA transactions.

[34]  Mahardhika Pratama,et al.  Parsimonious Network based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis , 2018, ArXiv.

[35]  Jing Lin,et al.  Periodicity-Impulsiveness Spectrum Based on Singular Value Negentropy and Its Application for Identification of Optimal Frequency Band , 2019, IEEE Transactions on Industrial Electronics.

[36]  Qiang Miao,et al.  Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time–frequency atom dictionary , 2018, Mechanical Systems and Signal Processing.

[37]  Robert B. Randall,et al.  Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference , 2016 .

[38]  Yanyang Zi,et al.  Sparsity-based Algorithm for Detecting Faults in Rotating Machines , 2015, ArXiv.