Abstract
Abstract For research in the fields of engineering asset management (EAM) and system health, relevant data resides in the information systems of the asset owners, typically industrial corporations or government bodies. For academics to access EAM data sets for research purposes can be a difficult and time-consuming task. To facilitate a more consistent approach toward releasing asset-related data, we have developed a data risk assessment tool (DRAT). This tool evaluates and suggests controls to manage, risks associated with the release of EAM datasets to academic entities for research purposes. Factors considered in developing the tool include issues such as where accountability for approval sits in organizations, what affects an individual manager’s willingness to approve release, and how trust between universities and industry can be established and damaged. This paper describes the design of the DRAT tool and demonstrates its use on case studies provided by EAM owners for past research projects. The DRAT tool is currently being used to manage the data release process in a government-industry-university research partnership.
Highlights
Engineering asset management (EAM) encompasses the processes, systems and human factors involved in managing the life cycle of engineering assets and the systems within which these assets operate
We suggest that datasharing in the field of EAM is sufficiently problematic as to be reducing the progress of the field by restricting researchers’ access to data required to develop effective models for asset life prediction and to improve maintenance management practice
This paper describes a process, called data risk assessment tool (DRAT), that enables industry partners to assess and control the risks associated with releasing EAM datasets to university research partners
Summary
Engineering asset management (EAM) encompasses the processes, systems and human factors involved in managing the life cycle of engineering assets and the systems within which these assets operate. Over the past 10 years, academia has been seeing an increasing push toward open science; this paradigm is reliant on “open data,” with increasing encouragement from publishers and funding government institutions for authors to release the data from which their research conclusions are derived (Sikorska et al, 2016; European Commission, 2020; ODI, 2020) In some industries, such as genomics, astrophysics, epidemiology, and geospatial research, this move has been embraced because it has enabled research that could not have been performed otherwise; the effort and costs to acquire such large datasets are too prohibitive for any one institution. The challenge of how to assess and manage the risks of sharing data pertaining to asset performance, for EAM researchers, is the focus of this paper
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