Posters - WindEurope Annual Event 2024

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Come meet the poster presenters to ask them questions and discuss their work

We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in the academic community. We look forward to seeing you there!

PO159: Anomaly detection of wind turbine bearings through vibration signal processing and deep learning

Emerson Lima, Head of Condition Analysis Center, AQTech Power Prognostics


Wind turbines play a pivotal role in the global renewable energy landscape, serving as stalwarts in the quest for clean and sustainable power generation. These turbines harness the kinetic energy of the wind and convert it into electricity. Consequently, ensuring the efficient operation and reliability of wind turbines is of paramount importance. The utilization of trend vibration data offers valuable insights into the condition of wind turbine components. As wind turbines operate in varying conditions, they are subjected to a range of stresses and strains. A key focus of this study is the application of trend vibration data analysis in tandem with advanced machine learning models, specifically Deep Autoencoder, Isolation Forest, and Principal Component Analysis (PCA). Anomalies in vibration patterns can signify issues such as misalignments, bearing wear, gear failures, or other abnormalities that may jeopardize turbine performance and longevity. The combination of these machine learning models demonstrates an improvement in anomaly detection accuracy, enabling early fault detection and facilitating timely maintenance interventions. This not only enhances the operational reliability of wind turbines but also optimizes resource allocation for maintenance activities, resulting in cost savings.

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