Deep learning for wind turbine fault diagnosis: advances, challenges, and future perspectives

Abstract With the continuous expansion of wind turbine scale, their fault identification and operation & maintenance (O&M) work are facing increasingly severe challenges. Traditional fault diagnosis methods, characterized by complex system modeling, high costs, and limited generalization capabilities, struggle to meet the requirements of large-scale applications. As an emerging intelligent technology, deep learning can automatically extract potential patterns from massive operational data, significantly reducing manual dependence. Consequently, it has garnered widespread attention in recent years and been applied in practice. This paper systematically reviews the research progress of deep learning in the field of wind turbine fault diagnosis, focusing on elaborating the principles and learning strategies of mainstream deep learning model architectures applicable to this domain. Through in-depth case analysis of two types of application scenarios—component-oriented and task-oriented—this paper reveals the breakthrough progress achieved by deep learning in fault diagnosis, and details its practical application processes in fault detection, diagnosis, and prediction through specific cases. Despite the advantage of high diagnostic accuracy and its role in advancing intelligent fault diagnosis, this paper critically points out that current research still faces challenges such as insufficient data, high model complexity, poor interpretability, and limitations in industrial on-site deployment. On this basis, future research should focus on breaking through key directions including few-shot learning, model lightweighting, cross-domain adaptation, and improved interpretability, while calling for the establishment of public datasets and unified evaluation standards. Only by addressing these challenges through interdisciplinary collaboration can the large-scale and reliable application of deep learning in wind power intelligent O&M be realized, thereby supporting the cost reduction, efficiency improvement, safe, and stable operation of the wind power industry. In summary, this paper provides a systematic reference for the further research and application of deep learning in the field of wind turbine fault diagnosis.