摘要
通风机的特殊性决定了煤矿中要求设备连续24 h工作,对其运行稳定性提出了非常高的要求。以煤矿中的FBCDZ-10-No36轴流式通风机为对象,采用BP神经网络,构建了通风机运行状态监测及报警系统。系统主要由通风机现场硬件、上位机软件以及工业以太网3大部分构成。系统利用振动加速度传感器对关键位置的振动状态信息进行采集,然后提取特征参量,基于训练好的BP神经网络结构对振动特征参量进行分析,获得通风机的故障类型。系统检测到故障问题后可以向外发出警报。将设计的系统部署到通风机工程实践中,经现场测试运行,发现各项功能都能够实现,通风机故障率大幅度降低,取得了很好的效果。
The particularity of the fan determines that the equipment in the coal mine is required to work continuously for 24 hours,which puts forward very high requirements for its operation stability.Taking the FBCDZ-10-No36 axial flow fan in the coal mine as the object,using the BP neural network,the fan operation status monitoring and alarm system was constructed.The system is mainly composed of three parts:fan site hardware,host computer software and industrial Ethernet.The system uses the vibration acceleration sensor to collect the vibration state information of the key position,then extracts the characteristic parameters,analyzes the vibration characteristic parameters based on the trained BP neural network structure,and obtains the fault type of the ventilator.When the system detects a fault problem,it can send out an alert.The designed system was deployed in the fan engineering practice,and after on-site test operation,it was found that all functions could be realized,and the fan failure rate was greatly reduced,and good results were achieved.
作者
赵凯
徐梦雅
Zhao Kai;Xu Mengya(Xi′an Aeronautical Polytechnic Institute,Yanliang 710089,China)
出处
《能源与环保》
2022年第8期271-276,共6页
CHINA ENERGY AND ENVIRONMENTAL PROTECTION
基金
西安航空职业技术学院校级基金(20XHZY-11)。
