Non-contact sensing for anomaly detection in wind turbine blades: a focus-SVDD with complex-valued auto-encoder approach

Frusque, G., Mitchell, D. , Blanche, J., Flynn, D. and Fink, O. (2024) Non-contact sensing for anomaly detection in wind turbine blades: a focus-SVDD with complex-valued auto-encoder approach. Mechanical Systems and Signal Processing, 208, 111022. (doi: 10.1016/j.ymssp.2023.111022)

Abstract

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally 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 – cross sectional analysis – of these complex structures during assembly and curing. In this paper, we enhance the quality assurance of WTB manufacturing utilising 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 characterises 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. The code and dataset will be made publicly available. The code and dataset are available here 1.

• Item Type: Articles
• Additional Information: This study was financed by the Swiss Innovation Agency (Innosuisse) under grant number: 47231.1 IP-ENG and the ORCA Hub [EP/R026173/1].
• Keywords: FMCW radar, non-destructive evaluation, complex-valued neural network, anomaly detection, analytical representation.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Blanche, Dr Jamie and Flynn, Professor David and Mitchell, Daniel
• Authors: Frusque, G., Mitchell, D., Blanche, J., Flynn, D., and Fink, O.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Mechanical Systems and Signal Processing
• Publisher: Elsevier
• ISSN: 0888-3270
• ISSN (Online): 1096-1216
• Published Online: 20 December 2023
• Copyright Holders: Copyright © 2023 Crown Copyright
• First Published: First published in Mechanical Systems and Signal Processing 208: 111022
• Publisher Policy: Reproduced under a Creative Commons License