基于小波和概率神经网络的煤矿主通风机故障诊断

Main Coal Mine Ventilator Fault Diagnosis Based on Wavelet and PNN

针对煤矿主通风机故障与征兆对应关系复杂的特点,以及利用传统BP网络进行故障诊断存在训练速度慢、易陷入局部极小的缺点,提出基于小波和概率神经网络的故障诊断方法:先利用时频两域有紧支撑能力的MexicanHat小波变换故障信号并提取能量归一化故障特征向量;然后将概率神经网络作为诊断决策分类器,输出故障模式。该方法充分利用了概率神经网络计算简单、收敛快、新增样本无须重新训练的特点,而且通过小波特征提取有效地减少了网络输入层节点数,降低了网络规模,减少了计算复杂度,加快了训练速度。经验证,此方法准确地诊断煤矿主通风机故障类型,具有速度快、精确度高的特点。

Owing to the complicated relationship between the faults and the corresponding symptoms of coal mine main ventilator the general BP neural network＇ s shortcomings such as lowT learning speed, probability of local minimum point. A fault diagnosis based on wavelet and PNN was introduced, which firstly uses Mexican Hat wavelet to transform diagnosis signal and extract energy normalized vector, then uses PNN to output fault as a classifier. This method utilizes the PNN＇ s advantages, such as simple structure, fast speed, the new trained samples can be added to PNN easily. Moreover, it decrease node numbers, network scale, and computation complexity and accelerate the training speed by way of extracting energy normalized vector. This way can diagnose the main coal mine ventilator＇s type quickly and accurately was proved by actual examples.