Knowledge graphs for operational decision-making in industrial maintenance: A systematic review

Abstract

Operational-level maintenance is essential to ensuring the normal functions of systems and equipment in the industry. The current practice for decision-making for operational maintenance is still expert-led and heavily relies on experts’ implicit knowledge from their experience. This leads to the urgent need for better knowledge management, which can facilitate more efficient and effective operational maintenance. To fulfill this need, knowledge graphs (KGs) have been proposed by previous studies, due to their ability to digitize knowledge and their structural and semantic features to facilitate various decision-making approaches. Although multiple KGbased methods have been proposed and implemented, several questions about maintenance-related knowledge graphs remain unclear, including 1) What methods are employed to exploit the capabilities of KGs in maintenance decision-making, 2) In which scenarios KGs are applied, and how they are utilized, and 3) What are the challenges and opportunities of implementing KGs in practice. To address these questions, this study made a comprehensive systematic review. A total of 270 papers were retrieved from Scopus, and 76 papers were closely examined. The review finds that 1) KG-based querying, reasoning, and enhancement of other algorithms are common methods in decision-making problems; 2) most KGs are applied in fault diagnosis, but KGs also contribute to maintenance action identification and organization; and 3) most applications of KGs in industrial maintenance decision-making are limited to simple tasks and more exploration of efficiently using and sharing KGs is needed. This study summarizes the state-of-the-art works in knowledge graphs for operational maintenance, which can facilitate further research.