Data-Oriented Predictive Maintenance for Museum Buildings: A Case Study of Hamedan’s Sacred Defense Museum Garden Using Digital Twins

proposes a data-oriented predictive maintenance (PdM) framework for museum buildings, using digital twins to monitor and optimize their condition. Museum buildings are a precious cultural heritage that needs special care, but they face many problems in their maintenance, such as high costs, low efficiency, and complex data management. The framework uses technologies like building information modeling, deep learning, machine vision, the Internet of Things, and blockchain to address these problems and improve the maintenance process. The paper uses Hamedan's Sacred Defense Museum Garden as a case study and describes the design, implementation, and evaluation of the framework. The paper also discusses the results, implications, limitations, and future directions of the research. The paper shows that the framework can reduce costs and risks, increase sustainability and efficiency, create and manage detailed digital models, secure and verify data transactions, and consider ethical and social aspects of PdM and digital twins for museum building maintenance. The paper is consistent with previous studies on PdM and digital twins and has important implications for museum building maintenance and cultural heritage preservation.

Data-Oriented Predictive Maintenance for Museum Buildings: A Case Study of Hamedan’s Sacred Defense Museum Garden Using Digital Twins Keywords:

data-oriented predictive maintenance , digital twins , museum buildings , cultural heritage , Hamedan’s Sacred Defense Museum Garden.