Intelligent Techniques Employing LSTM and ANFIS Models for Fault Detection and Isolation in a Wind Turbine Machine
Author:
Affiliation:
1. University of Djelfa,Laboratory of Applied Automatic and Industrial Diagnostics (LAADI),Djelfa,Algeria
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10419379/10419198/10419592.pdf?arnumber=10419592
Reference24 articles.
1. An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems
2. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings
3. Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques
4. Data driven sensor and actuator fault detection and isolation in wind turbine using classifier fusion
5. Sensitivity analysis for evaluation of the effect of sensors error on the wind turbine variables using Monte Carlo simulation
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Cross-turbine fault diagnosis for wind turbines with SCADA data: A spatio-temporal graph network with multi-task learning;2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC);2024-06-07
2. Fault Diagnosis Technique Applied to Pitch System Sensors of a Wind Turbine;2024 21st International Multi-Conference on Systems, Signals & Devices (SSD);2024-04-22
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