煤矿机电设备的远程监控与故障诊断系统研究

Research on the remote monitoring and fault diagnosis system of coal mine electromechanical equipment

在深入分析煤矿环境中设备特殊性的基础上,本研究充分整合信号处理、机器学习和深度学习等技术,设计并实现了一套高效的远程监控与故障诊断系统。该系统通过自动化数据采集和智能化故障诊断,有效弥补传统监控方法存在的人力资源投入大、监测覆盖面窄等问题。应用先进的深度学习模型,系统实现了对设备状态的实时监测和自动故障诊断,提高了监测的准确性和及时性。研究结果显示,该系统在实际应用中成功实现了全面监控、自动化故障诊断和预测预警,为煤矿机电设备的管理提供了创新性解决方案,推动了煤矿行业的数字化转型。

On the basis of in-depth analysis of the particularity of equipment in the coal mine environment,this study fully integrates signal processing,machine learning and deep learning technologies,and designs and realizes a set of efficient remote monitoring and fault diagnosis system.Through automatic data collection and intelligent fault diagnosis,the system can effectively make up for the problems of traditional monitoring methods,such as large human resources investment and narrow monitoring coverage.Using advanced deep learning model,the system realizes real-time monitoring and automatic fault diagnosis,and improves the accuracy and timeliness of monitoring.The research results show that the system has successfully realized the comprehensive monitoring,automatic fault diagnosis and prediction and early warning in the practical application,provided innovative solutions for the management of coal mine mechanical and electrical equipment,and promoted the digital transformation of the coal mine industry.