Journal of Electrical Engineering and Automation

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.

Industry 4.0; Smart factory; Electromechanical equipment; Predictive maintenance; Artificial intelligence