Data Anonymization for Maintenance Knowledge Sharing

Abstract

Formerly considered part of general enterprise costs, industrial maintenance has become critical for business continuity and a real source of data. Despite the heavy investments made by companies in smart manufacturing, traditional maintenance practices still dominate the industrial landscape. Maintenance knowledge sharing between industries can significantly optimize maintenance activity and improve process efficiency. Different international standards and initiatives are promoting such an approach. However, this trend failed to gain ground in the manufacturing industry. In this article, we present the results of our investigation about the real roadblocks that obstruct the progress of the maintenance knowledge sharing approach. We determined that knowledge graphs and, more importantly, automated data anonymization techniques can facilitate the development of general-purpose solutions to share maintenance knowledge among concerned actors.