基于典型样本数据融合方法的锅炉制粉系统故障诊断

Fault Diagnosis of a Boiler Milling System on the Basis of a Typical-swatch Data Fusion Method

针对D S证据理论应用中证据对目标模式的信度函数分配难以确定的问题,提出一种基于典型样本的信度函数分配获取方法,利用证据与各个目标模式典型样本之间的汉明距离构造信度函数分配,满足信度函数分配的定义并且减少了其主观性。将此数据融合方法应用到锅炉制粉系统故障诊断中,用以识别磨内煤粉自燃、磨满煤和磨断煤故障,根据历史数据验证,该方法可以有效判别故障类型并且能够做出早期诊断故障预测。

It is difficult for the evidence in a D-S evidence theory application to determine the distribution of target-mode confidence function. In the light of this problem the authors have proposed a typical swatch-based method for the acquisition of confidence function distribution. This method utilizes the Hamming distance between the evidence and the typical swatch of each target mode to construct the distribution of confidence function, thus meeting the definition of confidence function distribution and reducing its subjectivity. By using this data-fusion method in the fault diagnosis of a boiler milling system it is feasible to identify such pulverizer malfunctions and faults as pulverized coal self- ignition, pulverizer being full of coal and empty of coal, etc. As verified by historical data, the method under discussion can effectively recognize various types of faults and make an early prediction and diagnosis of ensuing malfunctions.