基于深度学习的地下空间机电设备智能维保系统的设计与应用
Design and Application of Intelligent Maintenance System for Mechanical and Electrical Equipment in Underground Space Based on Deep Learning
司贤举1
作者信息
- 1. 金肯职业技术学院,江苏南京 210000
- 折叠
摘要
设计了一种基于深度学习的地下空间机电设备智能维保系统.该系统采用堆叠降噪自编码器作为核心的故障检测模型,通过学习设备运行数据的内在规律,以实现对潜在故障的有效检测,基于检测结果及时对机电设备实施维护措施.结果表明,该系统在各种地下空间机电设备的故障检测任务上呈现了良好的性能,具有较高的应用潜力.
Abstract
An intelligent maintenance system for electromechanical equipment in underground space based on deep learning is designed.The system uses stacked noise reduction self encoder as the core of the fault detection model,through learning the inherent law of equipment operation data,to achieve effective detection of potential faults,and timely implement maintenance measures for electromechanical equipment based on the detection results.The results show that the system has good performance in the fault detection task of various underground space electromechanical equipment,and has high application potential.
关键词
深度学习/智能维保/自编码器Key words
deep learning/intelligent maintenance/auto-encoder引用本文复制引用
基金项目
江苏地下空间智慧运维工程技术研究开发中心开放课题(jsdxkjzh-2023-35)
出版年
2024
NETL
NSTL
万方数据