摘要
为实现对设备表面温度状态的实时识别及相关分析的智能决策化,引入了改进的层次分析法(AHP),动态地对设备表面多个监测点进行相关分析,选择出反映设备温度状态的关键测点,同时建立Kohonen自组织特征映射神经网络,对关键测点温度序列值进行一段时间的更新跟踪融合识别,获取关键测点的温度状态以此来表明设备的温度状态。以牵引电机为例,用Matlab软件仿真分析,识别正确率为89%,有效地降低了火灾发生的误报率。
In order to realize intelligent recognition of temperature state and related analysis for devices surface temperature states,an improved Analytic Hierarchy Process（AHP） model was introduced,which could dynamically analyze the relevance among several measuring points of temperature and selected key measuring point which could reflect the temperature state for devices.At the same time,Kohonen Self-Organizing Feature Map（SOFM） neural network was established,which could update and follow and recognize temperature serials value of key measuring points during some time,so that show device temperature status.Take traction motor for example,Matlab software simulation analysis show its recognition rate is 89%,which effectively reduces the false positive rate of fire.
出处
《计算机应用》
CSCD
北大核心
2011年第2期473-477,共5页
journal of Computer Applications
关键词
地铁火灾
关键测点
温度状态识别
层次分析法
Kohonen自组织特征映射
牵引电机
subway fire
key measuring point
temperature state recognition
Analytic Hierarchy Process（AHP）
Kohonen Self-Organizing Feature Map（SOFM）
traction motor
