Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

By Meik Schlechtingen, Ilmar Ferreira Santos. 2011

362 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/9eaec5d70e0f93ef0dd0383b99011d37121ef666

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Wei Qiao
2. Yaguo Lei
3. Naipeng Li
4. Dingguo Lu

In Control and Systems Engineering

1. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. David Flynn
8. Wei Qiao

In Mechanics of Materials

1. Yaguo Lei
2. Naipeng Li
3. Ningbo Li
4. Liang Guo
5. Tao Yan
6. Jing Lin
7. Wei Qiao
8. Dingguo Lu

Citations in the corpus

• A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods (2015, neutral): “Therefore, training SVMs requires fewer data samples than training ANNs, and the trained SVMs can have higher accuracy and reliability in fault diagnosis of WTs [87].”
• Machine learning methods for wind turbine condition monitoring: A review (2019, background): “Schlechtingen et al. [109] investigated three models (regression, NNs and autoregressive NNs) that learn to approximate the normal bearing temperature using SCADA input signals such as power output, nacelle temperature, generator speed, generator stator temperature, etc.”
• A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems (2015, methodology): “in WT gearboxes have also been used for gearbox CMFD [33].”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, background): “[162] compared three different model-based approaches for two-stage division of wind turbines, i.”