Development of a digital twin-based fault diagnosis system for building equipment operation and maintenance
基于数字孪生的建筑设备运维故障诊断系统开发

摘要

随着建筑智能化的发展，建筑设备运维管理面临诸多挑战。为提升运维效率与故障诊断准确性，构建基于数字孪生的建筑设备运维故障诊断系统。该系统借助数字孪生技术，对建筑设备进行实时映射与模拟，整合多源数据。通过先进的故障诊断算法，如深度学习、机器学习等，实现设备故障的精准定位与预测。经实践验证，此系统能显著缩短故障诊断时间，降低运维成本，提高建筑设备运行的稳定性与可靠性，为智慧建筑运维提供有力支持。

Abstract

With the advancement of intelligent building systems, equipment operation and maintenance management faces multiple challenges. To enhance operational efficiency and diagnostic accuracy, this study proposes a digital twin-based fault diagnosis system for building equipment. The system utilizes digital twin technology to enable real-time mapping and simulation of equipment, integrating multi-source data. Through advanced fault diagnosis algorithms such as deep learning and machine learning, it achieves precise fault localization and prediction. Practical validation demonstrates that this system significantly reduces fault diagnosis time, lowers maintenance costs, and improves operational stability and reliability of building equipment, providing robust support for smart building operations.