摘要
针对传统单一燃气调压器故障诊断模型存在诊断精度较低和结果误判别率高等问题,提出一种经验小波变换(EWT)与改进D-S证据理论结合的故障诊断方法,对燃气调压器故障状态进行诊断。使用EWT对传感器采集数据进行预处理并计算各分量能量熵,将其作为以广义回归神经网络、Elman神经网络和灰关联熵分析3种模型为基础构建的混合诊断模型的输入变量。根据D-S证据理论建立3个模型的基本信度函数,实现故障信息的决策融合,并引入证据关联系数法对证据体决策重要度和冲突问题加权修正。实验结果表明:EWT与改进D-S证据理论模型的故障诊断准确率达95.0%,在平均误差、均方误差、最大误差百分比等方面均优于单一的广义回归神经网网络、Elman神经网络和灰关联熵分析模型。
Aiming at the problems of low diagnostic accuracy and high misjudgment of the traditional single gas regulator fault diagnosis model,a fault diagnosis method based on empirical wavelet transform and improved D-S evidence theory was proposed to diagnose the fault state of gas pressure regulator.EWT was used to preprocess the data collected by the sensor and the energy entropy of each component was calculated,which was used as the input vector of the mixed diagnostic model based on three models,namely generalized regression neural network,elman neural network and grey relational entropy analysis.Then,the basic probability assignment of three models was established according to D-S evidence theory to realize the decision fusion of fault information,and the evidence correlation coefficient method was introduced to correct the weight of the decision importance and the conflict problems of evidence bodies.Experimental results show that the diagnostic accuracy of EWT and the improved D-S evidence theory model reaches 95.0%,and it is better than the single model GRNN,Elman and GREA in terms of mean error,mean square error and maximum error percentage.
作者
黄敬轩
王亚慧
HUANG Jingxuan;WANG Yahui(School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Key Laboratory of Intelligent Processing for Building Big Data,Beijing 100044,China)
出处
《机床与液压》
北大核心
2023年第7期199-207,共9页
Machine Tool & Hydraulics
基金
国家重点研发计划支撑项目(2018YFC0807806)
北京建筑大学研究生创新项目(PG2021060)。
关键词
燃气调压器
故障诊断
经验小波变换
能量熵
D-S证据理论
证据关联系数
Gas pressure regulator
Fault diagnosis
Empirical wavelet transform
Energy entropy
D-S evidence theory
Evidence correlation coefficient
