Design of fault monitoring system for belt conveyor
Belt conveyor is one of the most important equipment in the blast furnace delivery system.The failures of belt conveyor can lead to production losses and even safety accidents in severe cases.In order to discover the abnormalities of belt conveyor system in time,a fault monitoring system is de-signed for key components of the conveyor,including the motor,the reducer,the bearing of the bal-ance weigh's bend pulley,the bearing of the tail pulley,etc.The hardware architecture addresses the challenges of wide distribution,numerous measuring points,and long distances in the star layout of the belt conveyor monitoring points.The use of integrated electronics piezo-electric(IEPE)interface sensors effectively reduces wiring costs while enhancing signal transmission distance and reliability.On the software side,an innovative approach is employed,involving decompose the feature of raw training data through variational mode decomposition before training the neural network.Experimental results show that,without increasing the complexity of the neural network,the software's accuracy in judgment has increased from 96.5％to 99.3％,while the false negative rate has decreased from 3.5％to 0.7％.Additionally,training errors can converge rapidly,leading to a significant improve-ment in performance.
belt conveyorfault monitoringneural network algorithmtemperaturevibrationsensor
万方数据
维普
