分析了煤矿通风机的常见故障,在介绍BP神经网络原理和算法的基础上,建立了基于BP神经网络模型的通风机故障诊断模型,并应用数学软件MATLAB中的神经网络工具箱实现了通风机不同故障状态的识别。研究结果表明,该方法能准确地诊断通风机的故障类型,具有简单、准确的特点,为实现煤矿通风机的故障预警和保障煤矿的安全生产具有指导意义。 Based on the introduction of the principle and algorithm of BP neural network, the model of ventilator fault diagnosis based on BP neural network model is established. The neural network toolbox of MATLAB is used to realize the ventilator Identification of different fault conditions. The results show that this method can accurately diagnose the type of fan failure, with simple and accurate features, which is instructive for the early warning of coal mine fan failure and the safe production of coal mines.