Posters - WindEurope Annual Event 2024

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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!

PO259: A Comparative Study: Pre-whitening for Informative Kurtogram based Preprocessing in Bearing Fault Diagnosis using Convolutional Neural Networks

Atabak Mostafavi, Researcher, Fraunhofer Institute for Structural Durability and System Reliability LBF


Recent technology has made it possible for wind turbines to grow both in number and size, yet still their future depends on a competitive energy production price. An effective condition monitoring not only increases the lifetime expectancy of wind turbines but also reduces their downtimes, making wind energy a more reliable and efficient sources of energy. Despite their vast usage, roller element bearings as element that carry key functionalities in wind turbines remain prone to faults, leading to failures and downtime. Many research activities have been dedicated during the last decade to unveil the true bearing health condition. The recent development of machine learning techniques enables us to tackle more complex systems such as the multi-dynamic machinery of wind turbines. Yet, an appropriate preprocessing technique is advantageous in separating effects of various dynamics to highlight fault signature. Kurtogram has been proven a powerful tool for in bearing fault detection as they provide an informative filter to distinguish the fault dynamics signature from the rest of the spectra. However, they remain susceptible to impulsiveness caused by other dynamics (e.g., imbalance in system) and the result might be deceptive detection of fault band width and center frequency. The issue would be crucial in an automated preprocessing technique for a machine learning based diagnostic system due to the absence of expert supervision who decides on exact parameters of filters. Consequently, a preliminary process is necessary in order to remove non relevant dynamics before looking at the kurtograms and a candidate for such a process is so-called Pre-Whitening. Due to the variety of techniques, a comparative study is necessary to assess advantages and disadvantages of each approach. Thus, this work has been dedicated to evaluate performance of well know pre-whitening techniques using a CNN model, and highlight their obstacle and opportunities.

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