# Vibration fault detection in wind turbines based on normal behaviour models without feature engineering

arXiv: https://arxiv.org/abs/2206.12452 · HTML: https://mlsanity.com/paper/2206.12452/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Multi-target normal behaviour models for wind farm condition monitoring](https://mlsanity.com/paper/2012.03074/) (2020)
- [Vibration Fault Diagnosis in Wind Turbines based on Automated Feature Learning](https://mlsanity.com/paper/2201.13403/) (2022)

## Built on by (most cited first, 3 of 3)

- [Wind turbine condition monitoring based on intra- and inter-farm federated learning](https://mlsanity.com/paper/2409.03672/) (2024)
- [Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning](https://mlsanity.com/paper/2212.03529/) (2022)
- [Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning](https://mlsanity.com/paper/2504.17709/) (2025)

## Abstract (arXiv)

Most wind turbines are remotely monitored 24/7 to allow for an early detection of operation problems and developing damage. We present a new fault detection method for vibration-monitored drivetrains that does not require any feature engineering. Our method relies on a simple model architecture to enable a straightforward implementation in practice. We propose to apply convolutional autoencoders for identifying and extracting the most relevant features from the half spectrum in an automated manner, saving time and effort. Thereby, a spectral model of the normal vibration response is learnt for the monitored component from past measurements. We demonstrate that the model can successfully distinguish damaged from healthy components and detect a damaged generator bearing and damaged gearbox parts from their vibration responses. Using measurements from commercial wind turbines and a test rig, we show that vibration-based fault detection in wind turbine drivetrains can be performed without the usual upfront definition of spectral features. Another advantage of the presented method is that the entire half spectrum is monitored instead of the usual focus on monitoring individual frequencies and harmonics.