Research On Intelligent Monitoring System Of Belt Conveyor Operation Status

At present,belt conveyor monitoring systems generally have problems such as low accuracy,false positives,false negatives,and poor real-time performance,especially with the development of belt conveyors towards long distances,large volumes,heavy loads and intelligence,therefore,how to improve the accuracy of fault diagnosis and build a reliable real-time monitoring and fault diagnosis system are currently urgent problems that need to be solved.Aiming at the current situation and shortcomings of the belt conveyor operating condition monitoring system,this paper proposes methods based on multi-source information fusion theory,kernel support vector machine(KSVM)and kernel extreme learning machine(KELM),an intelligent monitoring system for the operating status of the belt conveyor is established,which can effectively improve the accuracy of the fault diagnosis of the belt conveyor monitoring system.For belt conveyor slippage,roller jam,and fire,collect belt speed,motor current,and temperature of the belt around the drive roller to extract the main time-domain features.For belt conveyor slippage,roller jam,and fire,collect belt speed,motor current,and temperature of the belt around the drive roller to extract the main time-domain features,based on the Dempster-Shafer(D-S)evidence theory in multi-source information fusion theory,an accurate fault diagnosis method for belt conveyor faults based on KSVM and KELM is proposed,a belt conveyor information diagnosis fusion model is established to accurately judge the belt conveyor in real time operation status.Condition monitoring of important roller groups of belt conveyors, collecting vibration signals of roller bearings,extracting wavelet packet energy characteristics and time domain characteristics of vibration signals,a multi-feature fusion diagnosis method based on KELM is proposed,a KELM-based method for monitoring the status of roller bearings is established to monitor the running status of important roller groups of the belt conveyor in real time.The intelligent monitoring system for the operation status of the belt conveyor adopts a modular design,including an information acquisition module,an accurate fault diagnosis module,and an operation status information display module,a belt conveyor experiment platform is set up,and experiments are performed using Lab VIEW and Matlab software.Experimental results show that the system’s fault diagnosis accuracy rate can reach 97%,which effectively improves the accuracy of belt conveyor fault diagnosis,can identify fault types of belt conveyor in a timely and accurate manner,reduces manual maintenance costs,and improves the intelligent operation level of the belt conveyor.