矿井刮板输送机监测及故障诊断系统设计

Design of Monitoring and Fault Diagnosis System for a Mine Scraper Conveyor

由于煤矿井下环境复杂、刮板输送机功能繁琐等多重因素影响,刮板输送机故障诊断耗时长、困难大。基于此,设计了基于STM32单片机和RBF神经网络算法的多种传感器、多通道传输、抗干扰能力强的刮板输送机检测及故障诊断系统,通过地面及井下系统配合完成电机、减速器、链轮组、链条等多部件故障状态识别,提高故障处置效率,有效增强煤矿生产安全水平。

Due to multiple factors such as complex-underground environment of coal mine and cumbersome function of scraper conveyor,the fault diagnosis of scraper conveyor is time-consuming and difficult.Based on this,a scraper conveyor detection and fault diagnosis system with multiple sensors,multi-channel transmission and strong anti-interference capability based-on STM32 microcontroller and RBF neural network algorithm is designed to complete multi-part fault status identification of motor,reducer,sprocket set,chain,etc.through the cooperation of surface and underground systems to improve fault disposal efficiency and efiectively enhance the safety level of coal mine production.