Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors

Xu, Zifei; Mei, Xuan; Wang, Xinyu; Yue, Minnan; Jin, Jiangtao; Yang, Yang; Li, Chun

doi:10.1016/j.renene.2021.10.024

Title: Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors

Journal Article · 31 December 2021 · Renewable Energy

DOI: https://doi.org/10.1016/j.renene.2021.10.024 · OSTI ID:1868739

Xu, Zifei; Mei, Xuan; Wang, Xinyu; Yue, Minnan; Jin, Jiangtao;

; Li, Chun

Not Available

View Accepted Manuscript (Publisher)

Cite

Export

Save

Sponsoring Organization:: USDOE

OSTI ID:: 1868739

Journal Information:: Renewable Energy, Journal Name: Renewable Energy Journal Issue: C Vol. 182; ISSN 0960-1481

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (22)

Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks Xu, Zifei; Li, Chun; Yang, Yang Applied Soft Computing, Vol. 95 https://doi.org/10.1016/j.asoc.2020.106515	journal	October 2020
Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis Lin, Zhongwei; Chen, Zhenyu; Wu, Qiuwei Energy, Vol. 147 https://doi.org/10.1016/j.energy.2018.01.055	journal	March 2018
Effect of soil-structure interaction on wind-induced responses of supertall buildings with large pile groups Huang, Yajun; Gu, Ming; El Naggar, M. Hesham Engineering Structures, Vol. 243 https://doi.org/10.1016/j.engstruct.2021.112557	journal	September 2021
Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions Zhao, Bo; Zhang, Xianmin; Li, Hai Knowledge-Based Systems, Vol. 199 https://doi.org/10.1016/j.knosys.2020.105971	journal	July 2020
Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks Hao, Shijie; Ge, Feng-Xiang; Li, Yanmiao Measurement, Vol. 159 https://doi.org/10.1016/j.measurement.2020.107802	journal	July 2020
Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network Wang, Xin; Mao, Dongxing; Li, Xiaodong Measurement, Vol. 173 https://doi.org/10.1016/j.measurement.2020.108518	journal	March 2021
An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition Wang, Zirui; Wang, Jun; Wang, Youren Neurocomputing, Vol. 310 https://doi.org/10.1016/j.neucom.2018.05.024	journal	October 2018
An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis Huang, Wenyi; Cheng, Junsheng; Yang, Yu Neurocomputing, Vol. 359 https://doi.org/10.1016/j.neucom.2019.05.052	journal	September 2019
Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals Chen, Jinglong; Pan, Jun; Li, Zipeng Renewable Energy, Vol. 89 https://doi.org/10.1016/j.renene.2015.12.010	journal	April 2016
Fault diagnosis of wind turbine based on Long Short-term memory networks Lei, Jinhao; Liu, Chao; Jiang, Dongxiang Renewable Energy, Vol. 133 https://doi.org/10.1016/j.renene.2018.10.031	journal	April 2019
Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform Teng, Wei; Ding, Xian; Cheng, Hao Renewable Energy, Vol. 136 https://doi.org/10.1016/j.renene.2018.12.094	journal	June 2019
Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels Chang, Yuanhong; Chen, Jinglong; Qu, Cheng Renewable Energy, Vol. 153 https://doi.org/10.1016/j.renene.2020.02.004	journal	June 2020
Effects of different poses and wind speeds on wind-induced vibration characteristics of a dish solar concentrator system Zuo, Hongyan; Tan, Jiqiu; Wei, Kexiang Renewable Energy, Vol. 168 https://doi.org/10.1016/j.renene.2020.12.127	journal	May 2021
Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review Li, B.; Delpha, C.; Diallo, D. Renewable and Sustainable Energy Reviews, Vol. 138 https://doi.org/10.1016/j.rser.2020.110512	journal	March 2021
Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review Wang, Tianyang; Han, Qinkai; Chu, Fulei Mechanical Systems and Signal Processing, Vol. 126 https://doi.org/10.1016/j.ymssp.2019.02.051	journal	July 2019
Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine Chen, Zhuyun; Gryllias, Konstantinos; Li, Weihua Mechanical Systems and Signal Processing, Vol. 133 https://doi.org/10.1016/j.ymssp.2019.106272	journal	November 2019
Optimisation of grid connected hybrid photovoltaic–wind–battery system using model predictive control design Mbungu, Nsilulu T.; Naidoo, Raj; Bansal, Ramesh C. IET Renewable Power Generation, Vol. 11, Issue 14 https://doi.org/10.1049/iet-rpg.2017.0381	journal	October 2017
Fault Diagnosis of Wind Turbine Gearbox Based on Deep Bi-Directional Long Short-Term Memory Under Time-Varying Non-Stationary Operating Conditions Cao, Lixiao; Qian, Zheng; Zareipour, Hamidreza IEEE Access, Vol. 7 https://doi.org/10.1109/ACCESS.2019.2947501	journal	January 2019
Deep Ensemble Capsule Network for Intelligent Compound Fault Diagnosis Using Multisensory Data Huang, Ruyi; Li, Jipu; Li, Weihua IEEE Transactions on Instrumentation and Measurement, Vol. 69, Issue 5 https://doi.org/10.1109/TIM.2019.2958010	journal	May 2020
Long Short-Term Memory Hochreiter, Sepp; Schmidhuber, Jürgen Neural Computation, Vol. 9, Issue 8 https://doi.org/10.1162/neco.1997.9.8.1735	journal	November 1997
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang Sensors, Vol. 17, Issue 2 https://doi.org/10.3390/s17020273	journal	January 2017
An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox Jing, Luyang; Wang, Taiyong; Zhao, Ming Sensors, Vol. 17, Issue 2 https://doi.org/10.3390/s17020414	journal	February 2017

Similar Records

Recognition of ships based on vector sensor and bidirectional long short-term memory networks

Journal Article · 2020 · Applied Acoustics · OSTI ID:1693578

Li, Sichun; Yang, Shuyu; Liang, Jinghan

Modeling injection-induced fault slip using long short-term memory networks

Journal Article · 2024 · Journal of Rock Mechanics and Geotechnical Engineering · OSTI ID:2439800

Mital, Utkarsh; Hu, Mengsu; Guglielmi, Yves; +2 more

Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network

Journal Article · 2019 · Water (Basel) · OSTI ID:1613794

Miao, Qinghua; Pan, Baoxiang; Wang, Hao; +2 more

Title: Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors

Citation Formats

References (22)

Similar Records

Related Subjects