智能建筑电气设备预测性维护关键技术研究

Research on Key Technologies for Predictive Maintenance of Electrical Equipment in Intelligent Buildings

摘要:

随着智能建筑内部电气系统日趋复杂化与集成化,传统基于定期巡检的维护模式已难以满足高可靠性的运行要求。为提升电气设备运行的稳定性,文中聚焦于融合先进传感技术、智能诊断算法及移动通信技术的预测性维护策略,通过构建多源异构数据采集网络,开发基于深度学习的故障诊断模型,并结合时间序列分析实现设备剩余使用寿命的精准预测,最终提出了一种云、边、端协同的系统架构。研究旨在实现从数据感知到智能决策的闭环,为智能建筑电气设备的健康管理提供一种新的技术路径,改变传统被动响应的维护方式。

Abstract:

With the increasing complexity and integration of electrical systems in intelligent buildings, the traditional maintenance mode based on periodic inspection can no longer meet the operation requirements of high reliability. To improve the operation stability of electrical equipment, this paper focuses on the predictive maintenance strategy integrating advanced sensing technology, intelligent diagnosis algorithms and mobile communication technology. By constructing a multi-source heterogeneous data acquisition network, developing a deep learning-based fault diagnosis model, and combining time series analysis to achieve accurate prediction of remaining useful life of equipment, a cloud-edge-end collaborative system architecture is finally proposed. This research aims to realize a closed loop from data perception to intelligent decision-making, provide a new technical approach for health management of electrical equipment in intelligent buildings, and transform the traditional passive response maintenance mode.