基于神经网络的带式输送机火灾预警算法

Fire Pre-warning Algorithm of Belt Conveyor Based on Neural Network

针对传统的带式输送机火灾预警算法大多仅根据单一特征信号进行火灾预警而导致误报率较高的问题,提出了一种传统火灾预警算法与神经网络相结合的带式输送机火灾预警算法。该算法将多种传统火灾预警算法提取的单一特征变量作为神经网络的输入量,利用实验室所测得的样本数据来训练神经网络,并将神经网络的输出作为火灾预警算法的最终输出。实验结果表明,该算法能够有效识别出样本数据中的火灾预警状态,误报率低。

In order to solve the problem of high alarming error rate of traditional fire pre-warning algorithms of belt conveyor which alarm according to single characteristic signal, a fire pre-warning algorithm of belt conveyor was proposed which combines the traditional fire alarming algorithms with neural network. The individual characteristic variables extracted by the traditional algorithms are used as inputs of neural network, and the neural network is trained by use of sample data measured in laboratory. The output of the neural network is the final output of the fire pre-warning algorithm. The experimental result showed that the algorithm can effectively identify fire pre-alarming status of the sample data with low error rate.