科研速览 · Science Skim继续刷下去 · Keep skimming →
◆ Wind energy science2026-08-04· Open innovation

Fostering open science through a digital open innovation platform – structural health monitoring case study

Sarah Barber, Shun Wang, Francesc Pozo, Yolanda Vidal, Marcela Machado, Amanda Aryda Silva Rodrigues de Sousa, Jefferson da Silva Coelho, Xukai Zhang, Yu Hu, Arash Noshadravan, Theodoros Varouxis, Mahmoud Abdelhak, Ramin Ghiasi, Abdollah Malekjafarian

原始摘要(英文原文)· Original abstract
Abstract. Open science and open innovation practices based on digital platforms can help address the lack of digital maturity and data sharing in the wind energy sector. Some previous efforts to introduce open science and open innovation practices in wind energy have been based around the WeDoWind project, which fosters data sharing through the organisation and documentation of open challenges. In this work, a two-phase design thinking approach is introduced to transform WeDoWind from a platform for documenting and managing challenges (phase 1) to an open innovation ecosystem for fostering open science and open innovation in wind energy (phase 2). The feasibility of the new open innovation ecosystem for fostering open science and open innovation in wind energy is then evaluated. The feasibility study involves first defining the scope and goals, then defining the TELOS aspects (technical, economic, legal, operational, and scheduling) for evaluation, carrying out the case study, and finally ending with an evaluation of the TELOS aspects. The case study itself involves defining case study evaluation metrics, choosing the case study topic, setting up and managing a WeDoWind challenge (the ASCE-EMI Structural Health Monitoring for Wind Energy Challenge), and then evaluating the case study metrics. The challenge goal is to detect three fault events with the highest possible accuracy. Five solutions submitted to the challenge include the PyMLDA open-code method, a health index monitoring with variational autoencoders method, an unsupervised event classification using k-means clustering method, and an unsupervised damage detection method using a feature selection framework. The results show that the case study could be successfully used for comparing and evaluating different fault detection methods. Overall, WeDoWind is found to have strong governance, clear regulation, and promising scalability potential. However, further progress is required to make it financially sustainable, to ensure adoption of the results in the sector, and to ensure community engagement to reach the critical mass necessary for self-sustaining growth.
读原文 · Read the paper ↗

AI 追问PRO

登录后使用 AI 追问

讨论区

登录后参与讨论

相关论文 · Related

Fostering open science through a digital open innovation platform – structural health monitoring case study — 科研速览 Science Skim