Summary of Supervised Transfer Learning Framework For Fault Diagnosis in Wind Turbines, by Kenan Weber et al.
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines
by Kenan Weber, Christine Preisach
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a supervised transfer learning framework for fault diagnosis in wind turbines that operates in an Anomaly-Space. The framework is designed to tackle common challenges in fault diagnosis, such as the lack of labeled data and the need to build models for each domain. By leveraging SCADA data and vibration data, the authors create an Anomaly-Space that can be interpreted as anomaly scores for each component in the wind turbine, making each value intuitive to understand. The framework uses popular supervised classifiers like Random Forest, Light-Gradient-Boosting-Machines, and Multilayer Perceptron as metamodels for the diagnosis of bearing and sensor faults. The results show that the proposed framework is able to detect cross-domain faults with a high degree of accuracy using one single classifier. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn from each other’s experiences to fix problems in wind turbines. It creates a special space where data can be understood easily, even without labeled information. This makes it easier for machines to diagnose issues and make decisions. The approach uses popular machine learning tools like Random Forest and Multilayer Perceptron to identify problems with bearings and sensors. The results show that this method works well and can detect problems accurately. |
Keywords
» Artificial intelligence » Boosting » Machine learning » Random forest » Supervised » Transfer learning
