Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines

TL;DR

This paper introduces a supervised transfer learning framework operating in an anomaly space for wind turbine fault diagnosis, enabling accurate cross-domain fault detection with a single classifier, reducing the need for multiple models.

Contribution

The paper presents a novel supervised transfer learning approach in an anomaly space that improves cross-domain fault diagnosis in wind turbines using a single classifier.

Findings

Multilayer Perceptron achieved highest performance

Framework detects cross-domain faults with high accuracy

Single classifier suffices for fault diagnosis

Abstract

Common challenges in fault diagnosis include the lack of labeled data and the need to build models for each domain, resulting in many models that require supervision. Transfer learning can help tackle these challenges by learning cross-domain knowledge. Many approaches still require at least some labeled data in the target domain, and often provide unexplainable results. To this end, we propose a supervised transfer learning framework for fault diagnosis in wind turbines that operates in an Anomaly-Space. This space was created using SCADA data and vibration data and was built and provided to us by our research partner. Data within the Anomaly-Space can be interpreted as anomaly scores for each component in the wind turbine, making each value intuitive to understand. We conducted cross-domain evaluation on the train set using popular supervised classifiers like Random Forest, Light-Gradient-Boosting-Machines and Multilayer Perceptron as metamodels for the diagnosis of bearing and sensor faults. The Multilayer Perceptron achieved the highest classification performance. This model was then used for a final evaluation in our test set. The results show, that the proposed framework is able to detect cross-domain faults in the test set with a high degree of accuracy by using one single classifier, which is a significant asset to the diagnostic team.