摘要
为解决选煤厂带式输送机传统故障诊断方法存在效率较低、准确度不高、设备隐患预测性不强等问题,通过分析带式输送机结构特点和常见故障,利用AI卷积神经网络智能算法实现带式输送机故障智能快速诊断,优化传统的故障识别算法,利用智能算法可及时发现潜在故障,故障识别准确率高达98.4%。在山西某选煤厂进行现场调试和试运行,结果表明,带式输送机故障智能识别算法不仅可以准确提取明显异常特征,还能及时提取微弱异常特征信号,响应速度从传统的2.58 s缩短到0.97 s,取得了满意的应用效果。
In order to solve the coal preparation plant belt conveyor traditional fault diagnosis method is low,accuracy is not high,equipment hidden trouble predictive is not strong,this paper analyzes the belt conveyor structure characteristics and common faults,using AI convolutional neural network intelligent algorithm belt conveyor intelligent fault diagnosis,optimize the traditional fault identification algorithm,using intelligent algorithm can find the potential fault in time,fault identification accuracy is as high as 98.4%.After on-site commissioning and trial operation in a coal preparation plant in Shanxi Province,it is shown that the intelligent fault identification algorithm of belt conveyor can not only accurately extract the obvious abnormal features,but also extract the weak abnormal feature signal in time.The response speed is shortened from the traditional 2.58 s to 0.97 s,achieving satisfactory application effect.
作者
李永成
LI Yongcheng(Dongqu Coal Preparation Plant,Xishan Coal Power(Group)Co.,Ltd.,Gujiao,Shanxi 030200,China)
出处
《自动化应用》
2024年第7期33-35,38,共4页
Automation Application
关键词
煤矿
带式输送机
智能巡检
故障诊断
神经网络
coal mine
belt conveyor
intelligent inspection
fault diagnosis
neural network
作者简介
李永成,男,1980年生,助理工程师,从事机电设备的维护与自动化改造工作。
