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On the capacity of deep autoencoder-based normal behaviour models in wind turbine condition monitoring
Date
2026-03
Abstract
This study compares Deep Autencoder (AE)-based Normal Behaviour Models (NBM) for anomaly detection in Wind Turbine SCADA data. Using a proprietary industrial dataset, we evaluate performance under real world challenges, like class imbalance and unseen anomalies. We conduct a systematic comparison across unsupervised, semi-supervised, and supervised approaches, and examine the impact of auxiliary loss functions. Our results show that unsupervised Vanilla AEs struggle to separate normal and abnormal data, different from what the NBM literature claims about the effectiveness of unsupervised setups, as we obtain a significant increase in the Area Under the Curve (AUC) via its classification head. We propose an Adversarial Robust AE (ARAE) to improve detection in data-scarce scenarios. In settings with limited abnormal data, ARAE maintains stable recall at a 1:4 abnormal-to-normal ratio, outperforming other models under severe class imbalance. Based on these results, we recommend a Bottleneck architecture for scenarios with abundant labelled data and ARAE for those with scarce abnormal examples.
Supervisor
Description
Publisher
Scitepress -Science and Technology Publications, Lda
Citation
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Bui_2026_onthe.pdf
Adobe PDF, 489.64 KB
ULRR Identifiers
Funding code
Funding Information
Taighde Éireann– Research Ireland
Systems, Methods, Context (SyMeCo)
Systems, Methods, Context (SyMeCo)
Sustainable Development Goals
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License
Attribution-NonCommercial-ShareAlike 4.0 International