基于红外热成像的电厂输煤廊道火灾识别智能预警识别方法

Infrared Thermal Imaging-Based Fire Detection and Early Warning System for Coal Transport Corridors in Power Plants

本文针对当前燃煤电厂输煤廊道内早期火灾隐患难以预警的问题,提出了一种基于红外热成像的输煤廊道火灾智能预警识别方法。通过前景对比分析法对红外热成像探测区域进行温度变化特征提取,并利用温度场过程中的扩散区域不规则性和生长性,对温度场扩散速率的准确识别,依据温度变化和温度场扩散速率的变化异常,实现火灾早期预警判别。利用某电厂现场获取的红外热成像数据进行了实验验证,结果表明,相比于常规的设定异常温度的识别算法,本文提出的早期火灾识别算法显著提高了检测的稳定性,并有效降低了监控系统的误报和漏报率。

Aiming at the current problem that it is difficult to warn of early fire hazards in the coal trans-mission corridor of coal-fired power plants,this paper proposes an intelligent early warning identification meth-od for coal transmission corridor fire based on infrared thermal imaging.Through the prospect contrast analysis method of infrared thermal imaging detection area for temperature change feature extraction,and the use of the temperature field process of diffusion region irregularity and growth,the accurate identification of the tempera-ture field diffusion rate,based on the temperature change and temperature field diffusion rate of the change in the anomaly of the change of the fire,early fire warning is realized.Experimental verification was carried out using infrared thermal imaging data obtained from the site of a power plant,and the results show that,com-pared with the conventional identification algorithm for setting abnormal temperatures,the early fire identifica-tion algorithm proposed in this paper significantly improves the stability of detection and effectively reduces the rate of false alarms and omissions in the monitoring system.