Machine learning predicts maintenance of the Öresund Bridge
Cybercom was assigned to develop a demo of an machine learning (ML) application that can predict when and where maintenance should be performed.
Like many others, the Öresund Bridge is on the brink of a more digitalised future. In efforts related to this, a strategy has been drafted for maintaining the bridge by increasing the use of data-driven predictive maintenance. Predictive Maintenance (PdM) is based on machine learning (ML) and is an application of advanced AI technology.
The initial project in the effort was run by Cybercom, which was assigned to develop a demo of an ML application that can predict when and where maintenance should be performed. The method also gives the user information about vulnerabilities in the operating chain. The purpose of data-driven PdM is to replace manual status inspections and time-based activities and reduce corrective maintenance.
Goal 9: Industry, Innovation and Infrastructure. Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation (9.4 Upgrade infrastructure and retrofit industries to make them sustainable).