TY - JOUR
T1 - Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network
AU - Xia, Liqiao
AU - Liang, Yongshi
AU - Leng, Jiewu
AU - Zheng, Pai
N1 - Funding Information:
This research is partially funded by the Mainland-Hong Kong Joint Funding Scheme ( MHX/001/20 ), 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 State Key Laboratory of Ultra-Precision Machining Technology ( Project No. 1-BBR2 ), The Hong Kong Polytechnic University, HKSAR, China.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/4
Y1 - 2023/4
N2 - Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. With large-scale sensor systems in modern factories, much data will be captured during monitoring and maintenance of complex industrial equipment. Accumulated data facilitates maintenance planning becomes more thorough and timely. Recently, a knowledge graph (KG) was offered to handle large-scale, unorganized maintenance data semantically, resulting in better data usage. Some prior studies have utilized KG for maintenance planning with semantic searching or graph structure-based algorithms, nevertheless neglecting the prediction of potential linkage. To fill this gap, a maintenance-oriented KG is established firstly based on a well-defined domain-specific ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to predict potential solutions and explain fault in maintenance tasks. Lastly, a maintenance case of oil drilling equipment is carried out, where the proposed model is compared with other cutting-edge models to demonstrate its effectiveness in link prediction. This research is anticipated to shed light on future adoption of KG in maintenance planning recommendations.
AB - Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. With large-scale sensor systems in modern factories, much data will be captured during monitoring and maintenance of complex industrial equipment. Accumulated data facilitates maintenance planning becomes more thorough and timely. Recently, a knowledge graph (KG) was offered to handle large-scale, unorganized maintenance data semantically, resulting in better data usage. Some prior studies have utilized KG for maintenance planning with semantic searching or graph structure-based algorithms, nevertheless neglecting the prediction of potential linkage. To fill this gap, a maintenance-oriented KG is established firstly based on a well-defined domain-specific ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to predict potential solutions and explain fault in maintenance tasks. Lastly, a maintenance case of oil drilling equipment is carried out, where the proposed model is compared with other cutting-edge models to demonstrate its effectiveness in link prediction. This research is anticipated to shed light on future adoption of KG in maintenance planning recommendations.
KW - Graph neural network
KW - Knowledge graph
KW - Link prediction
KW - Maintenance management
KW - Predictive maintenance
UR - http://www.scopus.com/inward/record.url?scp=85145964190&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2022.109068
DO - 10.1016/j.ress.2022.109068
M3 - Journal article
AN - SCOPUS:85145964190
SN - 0951-8320
VL - 232
JO - Reliability Engineering
JF - Reliability Engineering
M1 - 109068
ER -