Researchers Catch-up hosted online from University of WA

The written descriptions of maintenance work orders capture vital information, such as indicators of failure modes and end of life events, which are directly tied to MTTF values. Despite their prevalence and the significance of the information contained within, work order descriptions are seldom used for routine analysis. In this presentation we will provide an interactive demonstration of Theme 1's work on constructing the very first knowledge graph for maintenance, while also showcasing our approach to developing an annotated training dataset of work orders with which to train state-of-the-art deep learning models. The knowledge graph interface built as part of this research allows for the visualisation and querying of work orders, presenting vital information about work done, as well as failure modes for each asset, in a highly accessible way.