Improving data sharing in wind energy - structural health monitoring case study

Abstract

A lack of data sharing in the wind energy sector presents a large barrier to increasing the value of wind energy through innovation. One way of improving data sharing is to make it “FAIR”: findable, accessible, interoperable and reusable. The FAIR Data Maturity Model is a tool developed by the Research Data Alliance that can be used to assess and improve the “FAIRness” of data, by quantifying the extent of its findability, accessibility, interoperability and reusability. In this work, we investigate how the FAIR Data Maturity Model could be applied to improve data sharing in the wind energy sector, via a structural health monitoring (SHM) case study. This case study is created as part of a WeDoWind challenge, and was chosen due to the high potential of SHM in reducing the costs of energy through predictive maintenance. WeDoWind is a framework for creating mutually beneficial collaborations, and the WeDoWind wind energy ecosystem is a growing ecosystem of diverse people all over the world sharing and exchanging knowledge and data. It is found that the FAIRness of the provided data set is limited due to the lack of community standards, and the absence of public data sharing services catering specifically to the wind energy context. However, the FAIR Data Maturity Model is successfully applied to improve the FAIRness of the data sets in the case study. A participant survey shows that this made data sharing easier in the context of a WeDoWind data sharing project. Finally, the project results in a set of recommendations for helping the wind energy community to improve the FAIRness of data.