TY - GEN
T1 - A Knowledge Graph-based Link Prediction for Interpretable Maintenance Planning in Complex Equipment
AU - Xia, Liqiao
AU - Zheng, Pai
AU - Li, Shufei
AU - Lyu, Pin
AU - Lee, C. K.M.
AU - Zhou, Jialiang
AU - Wang, Kaiye
N1 - Funding Information:
There are certain restrictions on this study, though. For instance, 1) language: the experimental KG's nodes and edges are described in Mandarin, where their graph embedding representation is constrained, and 2) insufficient failure analysis: this KG describes the failure in the cause nodes, whereas causal inference of the symptom can be expanded to include root cause, secondary cause, and superficial cause. This study may offer further information for using KG in PdM, it is thought. Furthermore, the KG might be improved by 1) including the temporal signal pattern and2) creating a user-friendly interface so that users can easily comprehend the maintenance planning. ACKNOWLEDGMENT This research is partially funded by the Mainland-Hong Kong Joint Funding Scheme (MHX/001/20), Shanghai Science and technology program (22010500900), Innovation and Technology Commission (ITC), Hong Kong Special Administration Region, National Key R&D Programs of Cooperation on Science and Technology Innovation with Hong Kong, Macao and Taiwan (SQ2020YFE020182), Ministry of Science and Technology (MOST) of the People’s Republic of China, and Centre for Advances in Reliability and Safety (CAiRS) admitted under AIR@InnoHK Research Cluster.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/8
Y1 - 2022/8
N2 - Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. Many data points will be collected during the monitoring and maintenance of sophisticated equipment thanks to the large-scale sensor systems installed in contemporary factories. As a result, with the help of collected maintenance data, maintenance plans may be more detailed and timelier. A knowledge graph (KG) has recently been proposed to manage massive and unorganized maintenance data semantically, enhancing data usage. Despite the fact that previous research had utilized KG for maintenance planning, they had only used semantic searching or graph structure-based algorithms and had not included the prediction of new links. To fill this gap, a maintenance-oriented KG is established firstly based on the well-defined ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to find the potential solutions and explain the fault, specifically for the heterogeneous and sparse graph structure of maintenance-orient KG. A maintenance case of oil drilling equipment is carried out, which compares the proposed model with other cutting-edge models to demonstrate its effectiveness in link prediction.
AB - Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. Many data points will be collected during the monitoring and maintenance of sophisticated equipment thanks to the large-scale sensor systems installed in contemporary factories. As a result, with the help of collected maintenance data, maintenance plans may be more detailed and timelier. A knowledge graph (KG) has recently been proposed to manage massive and unorganized maintenance data semantically, enhancing data usage. Despite the fact that previous research had utilized KG for maintenance planning, they had only used semantic searching or graph structure-based algorithms and had not included the prediction of new links. To fill this gap, a maintenance-oriented KG is established firstly based on the well-defined ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to find the potential solutions and explain the fault, specifically for the heterogeneous and sparse graph structure of maintenance-orient KG. A maintenance case of oil drilling equipment is carried out, which compares the proposed model with other cutting-edge models to demonstrate its effectiveness in link prediction.
KW - Graph neural network
KW - Knowledge graph
KW - Maintenance management
KW - Maintenance planning
KW - Predictive maintenance
UR - http://www.scopus.com/inward/record.url?scp=85143086436&partnerID=8YFLogxK
U2 - 10.1109/ICRMS55680.2022.9944561
DO - 10.1109/ICRMS55680.2022.9944561
M3 - Conference article published in proceeding or book
AN - SCOPUS:85143086436
T3 - 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022
SP - 301
EP - 305
BT - 13th International Conference on Reliability, Maintainability, and Safety
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022
Y2 - 21 August 2022 through 24 August 2022
ER -