The Reliability Paradox: Machine Learning Applications in Industrial Fans and the Perspectives of Industry Experts

The integration of Artificial Intelligence (AI) in turbomachinery and fan systems is transforming traditional design, diagnostics, and operational strategies. Artificial Intelligence allows for the efficient exploration of wide design space, easy and fast prediction of fan performance and improving existing system operation and maintenance. Nevertheless, this AI-driven revolution still raises concerns and diffidence in the community, as highlighted by the results of a survey delivered to over 100 fan experts and discussed in this paper. This manuscript aims to provide an overview of Fan-AI applications through a comprehensive literature review of notable use cases. The applications target different stages of the life cycle of fans, from ML-assisted three-dimensional design/optimization to data-driven performance prediction, AI-driven fan control and fault analysis/prognosis. For each of these categories, the relevant application are discussed, highlighting trends, adopted algorithms and strategies, as well as limiting factors. This study also shares the views of experts on both fan design, optimization and operations and AI methods in the upcoming challenges for fan industry. Starting from the need of high-quality data, the improvement of model generalization and the embedding of Fan-AI in the standard engineering practices. This paper concludes with a discussion on the future role of AI in fans, suggesting pathways for research and industrial adoption that balance technological innovation with domain-specific constraints.