Research on key technologies of predictive maintenance of electromechanical equipment in smart factory under industry 4.0
工业4.0背景下智能工厂机电设备预测性维护关键技术研究

摘要

在工业4.0背景下，智能工厂的建设对机电设备的高效稳定运行提出了更高要求。预测性维护作为关键手段，能够通过多源数据采集、实时状态监测与智能算法分析，实现设备故障的提前预警与维护优化。本文以设备全生命周期管理为逻辑主线，探讨传感技术、数据驱动建模与人工智能在预测性维护中的协同应用，重点分析其在故障模式识别、健康状态评估及决策支持中的作用，为实现智能工厂的高可靠性与低成本运维提供技术支撑。

Abstract

In the context of Industry 4.0, the construction of smart factories imposes higher demands on the efficient and stable operation of electromechanical equipment. Predictive maintenance, as a key approach, can achieve early equipment failure warnings and maintenance optimization through multi-source data collection, real-time condition monitoring, and intelligent algorithm analysis. This paper focuses on the entire lifecycle management of equipment as the logical thread, exploring the collaborative application of sensing technology, data-driven modeling, and artificial intelligence in predictive maintenance. It emphasizes their roles in fault pattern recognition, health status assessment, and decision support, providing technical support for achieving high reliability and cost-effective operation in smart factories.