Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples-Reference-Cited by-同舟云学术

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples

Published:2014-01 Issue: Volume:14 Page:447-460
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Schlechtingen Meik,Santos Ilmar Ferreira

Publisher

Elsevier BV

Subject

Software

Reference8 articles.

1. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: system description;Schlechtingen;Journal of Applied Soft Computing,2012

2. Vibration-based Condition Monitoring;Randall,2011

3. Wind turbine condition monitoring and reliability analysis by SCADA information;Yang;IEEE,2011

4. SIMAP: intelligent system for predictive maintenance application to the health condition monitoring of a windturbine gearbox;Sanz-Bobi;Computers in Industry,2006

5. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection;Schlechtingen;Mechanical Systems and Signal Processing,2011

Cited by 103 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. Unlocking the potential: A review of artificial intelligence applications in wind energy;Expert Systems;2024-08-27

2. Artificial Intelligence for safety and reliability: A descriptive, bibliometric and interpretative review on machine learning;Journal of Loss Prevention in the Process Industries;2024-08

3. Wind Power Prediction Based on Hybrid Neural Network and Wind Power Curve;2023 China Automation Congress (CAC);2023-11-17

4. A reliable framework for predicting wind turbine failures utilising SCADA and Alarm data;2023 5th International Conference on System Reliability and Safety Engineering (SRSE);2023-10-20

5. Condition Monitoring of Wind Turbine Pitch System Based on Informer;2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou);2023-10-12