# Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach

arXiv: https://arxiv.org/abs/2306.10808 · HTML: https://mlsanity.com/paper/2306.10808/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Unitary Evolution Recurrent Neural Networks](https://mlsanity.com/paper/1511.06464/) (2015)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [A Survey of Complex-Valued Neural Networks](https://mlsanity.com/paper/2101.12249/) (2021)
- [Complex-Valued Autoencoders for Object Discovery](https://mlsanity.com/paper/2204.02075/) (2022)
- [Complex-valued Spatial Autoencoders for Multichannel Speech Enhancement](https://mlsanity.com/paper/2108.03130/) (2021)
- [A Review: Challenges and Opportunities for Artificial Intelligence and Robotics in the Offshore Wind Sector](https://mlsanity.com/paper/2112.06620/) (2021)
- [Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems](https://mlsanity.com/paper/2302.12918/) (2023)

## Built on by (most cited first, 4 of 4)

- [NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation](https://mlsanity.com/paper/2311.11961/) (2023)
- [Semi-Supervised Health Index Monitoring with Feature Generation and Fusion](https://mlsanity.com/paper/2312.02867/) (2023)
- [Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers](https://mlsanity.com/paper/2507.19168/) (2025)
- [Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction](https://mlsanity.com/paper/2609.16314/) (2026)

## Abstract (arXiv)

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs and lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials. Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view of these complex structures during curing. In this paper, we enhance the quality assurance of manufacturing utilizing FMCW radar as a non-destructive sensing modality. Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave. (2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD). This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies. (3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD). EAD takes advantage of the Rayleigh distribution, which characterizes an instantaneous amplitude signal. The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods. This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application.