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该文利用航空发动机气路静电监测技术开展涡扇发动机试车静电监测试验,介绍了发动机试车台架所用的静电传感器和试车安装环境,采集到一些发动机异常状态下的静电信号数据。提出了一种基于静电信号排列熵值的气路状态异常检测方法对发动机气路异常状态进行检测,并与静电信号经典特征参数的分析结果进行对比。试验分析结果表明,排列熵指标对气路故障所导致的气路状态的突变比原始信号和经典特征参数更加敏感,同时,相较于传统特征参数排列熵指标能够更清晰地反映出燃烧故障进程中的故障变化情况。
In this paper, the static electricity monitoring test of turbofan engine is carried out by aeroengine electrostatic monitoring technology. The electrostatic sensor used in the engine test bench and the test installation environment are introduced. Some static signals of the engine are collected under abnormal conditions. An abnormal gas path detection method based on the entropy value of electrostatic signal is proposed to detect the abnormal state of the gas path of the engine and to compare with the analysis results of classical characteristic parameters of electrostatic signal. The experimental results show that the permutation entropy index is more sensitive to the sudden change of the gas path caused by the gas path fault than the original signal and the classical characteristic parameter. Meanwhile, compared with the traditional feature parameters, the entropy index can more clearly reflect the progress of the combustion fault In the fault changes.