通风机运行状态监测及预警系统设计与应用

某煤矿井下FBCDZ-10-No36轴流式通风机频繁发生电机、轴承、叶片等故障问题,严重影响通风机安全运行。针对性设计一套基于BP神经网络通风机运行状态监测及预警系统。该系统整合现场硬件监测、上位机软件处理及工业以太网通信,通过振动加速度传感器精确采集通风机关键部位振动数据,提取特征参数,利用BP神经网络进行故障类型分析和预警。实际应用效果表明,该系统应用后通风机故障率从7.2次/月降低至0.15次/月,大幅度降低通风机故障率,保障通风机的安全稳定运行,经济和安全效益显著。

Faults such as motors,bearings,and blades frequently occur in the FBCDZ-10-No36 axial flow fan in a coal mine,which seriously affects the safe operation of the fan.Aiming at this,a set of fan operating status monitoring and early warning system based on BP neural network is designed.The system integrates on-site hardware monitoring,upper computer software processing and industrial Ethernet communication.It accurately collects vibration data from key parts of the fan through vibration acceleration sensors,extracts characteristic parameters,and uses BP neural networks to analyze and warn fault types.The actual application results show that after the application of the system,the fan failure rate is reduced from 7.2 times/month to 0.15 times/month,which greatly reduces the fan failure rate,ensures the safe and stable operation of the fan,and has significant economic and safety benefits.