Open Access Datasets for Active Magnetic Bearing High-Speed Machinery

There is a notable shift towards data-based research, with an increasing emphasis on openly sharing research datasets. This trend aligns with the principles of open access (OA) and the FAIR (Findable, Accessible, Interoperable, and Reusable) data movement, which enhance the visibility and usability of research outputs. In several fields of study, this practice is already well established. When considering the development of high-speed technology (HST), particularly those based on magnetic suspension, the field is inherently multidisciplinary, involving various engineering disciplines to develop functional concepts. While the design phase often generates valuable data, including experimental data from integrated sensors, there remains a notable lack of openly shared data in this field. Before introducing our own HST data, this article provides a comprehensive literature review on data-driven research, FAIR data principles, and existing open data platforms used for data sharing to highlight the significant rise in data-driven research within engineering and justify the need for a dedicated repository for data sharing. Following this, we present a detailed description of the open-access datasets related to one HST application example system, including identification results, operational data, and design data. By making this data available in the selected repository, we aim to support research in control theory, modeling, and data-driven methodologies related to the development of high-speed technology based on magnetic suspension. The datasets discussed in this paper are openly available in the Harvard Dataverse repository (https://dataverse.harvard.edu/dataverse/luthstdata).