Abstract
The current approach for detecting anomalies in acceleration signals relies extensively on feature engineering. Indeed, detecting rotor imbalances in wind turbines starts by first isolating and then assessing the energy of the 1P harmonic, leading to a feature that is efficient but not failure mode agnostic. While different engineered features can be used concurrently, some anomalies in the acceleration signal might remain undetected by the algorithm, even though they are visually noticeable to a human in the signal’s spectrogram. Thus, this project aims to build an AI algorithm capable of detecting anomalies in spectrograms, agnostic of their origin, providing an early warning for potential structural issues. The proposed algorithm infers spectrograms of acceleration signals through a deep autoencoder. Anomalies are identified based on a custom reconstruction error. A sensitivity analysis is performed for two types of anomaly, in which waveforms with different energy levels are artificially added to an acceleration signal measured from an offshore wind turbine (OWT). For a 1P harmonic anomaly representing 20% of the total signal energy, the proposed approach yielded an efficiency (AUC) equal to 96% thanks to a novel reconstruction error, which significantly increased the performances.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Van der Veen, J.S., Van der Waaij, B., Meijer, R.J.: Sensor data storage performance: SQL or NoSQL, physical or virtual. In: 2012 IEEE Fifth International Conference on Cloud Computing, pp. 431–438. IEEE, June 2012
Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philos. Trans. Roy. Soc. A: Math. Phys. Eng. Sci. 365(1851), 303–315 (2007)
Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley (1977)
Tcherniak, D., Mølgaard, L.L.: Vibration-based SHM system: application to wind turbine blades. In: Journal of Physics: Conference Series, vol. 628, no. 1, p. 012072. IOP Publishing, July 2015
Kawaguchi, Y., et al.: Description and discussion on DCASE 2021 challenge task 2: unsupervised anomalous sound detection for machine condition monitoring under domain shifted conditions. arXiv e-prints arXiv:2106.04492, pp. 1–5 (2021)
Tanabe, R., et al.: MIMII DUE: sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions. arXiv e-prints arXiv:2006.05822, pp. 1–4 (2021)
Conradi Hoffmann, J.L., Horstmann, L.P., MartÃnez Lucena, M., Medeiros de Araujo, G., Fröhlich, A.A., Nishioka, M.H.N.: Anomaly detection on wind turbines based on a deep learning analysis of vibration signals. Appl. Artif. Intell. 35(12), 893–913 (2021)
Shao, H., Jiang, H., Zhao, H., Wang, F.: A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech. Syst. Signal Process. 95, 187–204 (2017)
Nie, X., Liu, S., Xie, G.: A novel autoencoder with dynamic feature enhanced factor for fault diagnosis of wind turbine. Electronics 9(4), 600 (2020)
Mao, J., Wang, H., Spencer Jr., B.F.: Toward data anomaly detection for automated structural health monitoring: exploiting generative adversarial nets and autoencoders. Struct. Health Monit. 1475921720924601 (2020)
De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The Mahalanobis distance. Chemom. Intell. Lab. Syst. 50(1), 1–18 (2000)
Yang, Y., Zha, K., Chen, Y.C., Wang, H., Katabi, D.: Delving into deep imbalanced regression. arXiv preprint arXiv:2102.09554 (2021)
Zheng, X., Wang, M., Ordieres-Meré, J.: Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0. Sensors 18(7), 2146 (2018)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420–434. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44503-X_27
Acknowledgement
This research has been performed in the framework of the Offshore Wind Infrastructure Project (OWI-Lab). The authors also acknowledge the financial support by the Fund for Smart Circular Bridge (SCB) Project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bel-Hadj, Y., Weijtjens, W. (2023). Anomaly Detection in Vibration Signals for Structural Health Monitoring of an Offshore Wind Turbine. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2022. Lecture Notes in Civil Engineering, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-031-07322-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-031-07322-9_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07321-2
Online ISBN: 978-3-031-07322-9
eBook Packages: EngineeringEngineering (R0)