Wind turbine blade bearing fault detection with Bayesian and Adaptive Kalman Augmented Lagrangian Algorithm

[1]  Bo-Suk Yang,et al.  Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions , 2021, IEEE Transactions on Industry Applications.

[2]  Xiaoquan Tang,et al.  Wind Turbine Blade Bearing Fault Diagnosis Under Fluctuating Speed Operations via Bayesian Augmented Lagrangian Analysis , 2020, IEEE Transactions on Industrial Informatics.

[3]  Xingxing Jiang,et al.  Multi-source fidelity sparse representation via convex optimization for gearbox compound fault diagnosis , 2020 .

[4]  Zepeng Liu,et al.  Naturally Damaged Wind Turbine Blade Bearing Fault Detection Using Novel Iterative Nonlinear Filter and Morphological Analysis , 2020, IEEE Transactions on Industrial Electronics.

[5]  Zepeng Liu,et al.  Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis , 2020, IEEE Transactions on Instrumentation and Measurement.

[6]  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.

[7]  Weiguo Huang,et al.  Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis , 2019, IEEE Transactions on Instrumentation and Measurement.

[8]  Zhibin Zhao,et al.  Data‐driven multiscale sparse representation for bearing fault diagnosis in wind turbine , 2019, Wind Energy.

[9]  Yi Sun,et al.  A Data-Driven Approach for Bearing Fault Prognostics , 2019, IEEE Transactions on Industry Applications.

[10]  Jun Wang,et al.  Wind Turbine Bearing Fault Diagnosis Based on Sparse Representation of Condition Monitoring Signals , 2019, IEEE Transactions on Industry Applications.

[11]  Xiaoquan Tang,et al.  Bayesian augmented Lagrangian algorithm for system identification , 2018, Syst. Control. Lett..

[12]  Zhiwei Gao,et al.  A reinforcement learning based fault diagnosis for autoregressive-moving-average model , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[13]  R. Amano Review of Wind Turbine Research in 21st Century , 2017 .

[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]  Jerry L. Hudgins,et al.  Bearing Fault Diagnosis of Direct-Drive Wind Turbines Using Multiscale Filtering Spectrum , 2017, IEEE Transactions on Industry Applications.

[16]  Li Lin,et al.  Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network , 2016, 2016 IEEE International Conference on Aircraft Utility Systems (AUS).

[17]  P. S. Heyns,et al.  Fault detection of slow speed bearings using an integrated approach , 2015 .

[18]  Shuangwen Sheng,et al.  Report on Wind Turbine Subsystem Reliability - A Survey of Various Databases (Presentation) , 2013 .

[19]  Robert B. Randall,et al.  Order tracking for discrete-random separation in variable speed conditions , 2012 .

[20]  Ruwen Chen,et al.  General expression for linear and nonlinear time series models , 2009 .

[21]  David Mba,et al.  Development of Acoustic Emission Technology for Condition Monitoring andDiagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and RotatingStructures. , 2006 .

[22]  Robert B. Randall,et al.  Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation) , 2005 .

[23]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm , 2004 .

[24]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms , 2004 .

[25]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[26]  Jr. S. Marple,et al.  Computing the discrete-time 'analytic' signal via FFT , 1999, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).