基于BP神经网络的容错控制技术在通风机监控系统中的应用  

Application of Fault Tolerant Control Based on BP Neural Network in Monitor and Control System of Mine Ventilator

作　　者：付娟娟[1] 李天玉[1] 侯秀杰[1] 

机构地区：[1]中国矿业大学信息与电气工程学院,江苏徐州221008

出　　处：《煤矿机械》2015年第1期220-222,共3页Coal Mine Machinery

摘　　要：矿井通风机监控系统有效地保障通风机的正常运行,针对监控系统中众多的传感器和执行器故障,BP神经网络对正常状态和故障状态分别进行训练,将得到的网络结构、权值和阈值进行存储,再通过容错控制中的重构,对故障点进行诊断和处理。利用风量传感器进行了仿真实验,证明基于BP神经网络的容错控制可以有效地修正传感器故障,从而推广至监控系统的所有传感器。The monitor and control system of mine ventilator is established to guarantee the normal operation of the ventilator effectively. For the purpose of solving a large number of faults of the sensors and actuators, the normal and fault conditions were trained by the BP neural network. The network structures,weights and thresholds obtained were storaged in a computer. Through the reconstruction of fault-tolerant control,the failure points were got to be diagnosed and treated. This paper used air flow sensor to do the simulation experiments for proving that fault-tolerant control system based on BP neural network can correct sensor failure effectively and it can extend to all sensors of monitor system.

关 键 词：BP神经网络 容错控制 通风机监控系统 传感器 

分 类 号：TD441[矿业工程—矿山机电]