Data-driven decision-making for equipment maintenance

• Published in: Automation in construction 2020-04, Vol.112, p.103103, Article 103103
• Main Authors: Ma, Zhiliang, Ren, Yuan, Xiang, Xinglei, Turk, Ziga
• Format: Article
• Language: English
• Subjects: Building information modeling; Building management systems; Computer simulation; Decision making; Developing countries; Facility maintenance management; Geographic information system; Geographic information systems; Industrial parks; Industrial plants; Internet of Things; LDCs; Maintenance; Monte Carlo simulation; Reliability-centered maintenance; Renovation; Science parks
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2020.103103

Industrial parks, science parks, university campuses, tourist complexes are springing up greatly in developing countries. Decision-making on equipment maintenance of these areas where facilities intensively locate differs from that of industrial systems or plants in two features: 1) it needs regular repetition and update because equipment replacements, facility renovations and equipment condition changes often happen during the operation phase, 2) the accumulated maintenance data in these areas are large enough to be used for decision-making. However, existing approaches to decision-making on equipment maintenance have not considered such features. To fill this gap, this research aims to propose a data-driven approach for decision-making on equipment maintenance by integrating three technologies: Reliability Centered Maintenance (RCM), Building Information Modeling (BIM) and Geographic Information System (GIS). By implementing quantitative decision-making models and Monte-Carlo simulation, a data-driven RCM process is proposed. BIM and GIS are integrated to support the acquisition and update of data required for the proposed RCM process. Based on requirement analysis and architecture design, a prototype system is developed and verified by using a virtual campus. The results show that the proposed approach reduces labor cost and diminishes difficulties of decision-making on equipment maintenance. This paper contributes to the body of knowledge in that it provides an objective approach for the decision-making on equipment maintenance. For future improvements, the approach is compatible with the Internet of Things (IoT) technology to support more efficient data acquisition as a basis for decision-making on maintenance. •A data-driven RCM process for equipment management is proposed.•A maintenance management system is established based on the process.•A prototype system is developed and verified through an expert workshop.•It provides an objective approach for decision-making on equipment maintenance.