Abstract
The offshore wind industry has been gaining significant attention in recent years, as the world looks to transition to more sustainable energy sources. While the industry has successfully reduced costs and increased efficiency, there is still room for improvement in terms of safety for workers. Using machine learning (ML) and deep learning (DL) technologies can significantly improve offshore wind industry safety by facilitating better accident prediction and failure prevention. The current study aims to fill a significant gap in the existing literature by developing a useful selection of machine learning models for simple implementation in the offshore wind industry. These models will then be used to inform decision-making around safety measures, such as scheduling maintenance or repairs or changing work practices to reduce risk. The development of this tool has the potential to significantly contribute to the long-term viability of the offshore wind industry and the protection of its workers. By providing accurate predictions of potential accidents and failures, the tool can enable companies to take proactive measures to prevent incidents from occurring, reducing the risk of injury or death to workers and reducing the financial cost of accidents and downtime. The chapter concludes with a summary of the present study's research challenge and the literature gaps. It highlights the importance of developing effective machine learning models and implementing stricter data records to improve safety in the offshore wind industry and the potential impact of these tools on the long-term viability of the industry. The chapter also notes that the high performance of selected models proves the reliability of the expected predictions and demonstrates the effectiveness of machine learning models for decision- making around safety in the offshore wind industry.
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Barouti, B.D., Kadry, S. (2024). Application of Machine Learning to Improve Safety in the Wind Industry. In: Nayak, J., Naik, B., S, V., Favorskaya, M. (eds) Machine Learning for Cyber Physical System: Advances and Challenges. Intelligent Systems Reference Library, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-54038-7_5
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