摘要
针对柴油机缸套磨损故障诊断问题,在实车上测试了柴油机机体振动信号,应用经验模态分解(EMD)对不同磨损状态下的柴油机机体振动信号进行了分析,然而,EMD存在的模态混叠问题使其难以获得准确的基本模式分量(IMF).为此引入基于总体经验模态分解(EEMD)的改进的局域波分析方法,利用EEMD获取无模式混淆的IMF,通过Hilbert边际谱分析信号能量随瞬时频率的变化特征.工程实测分析结果验证了应用该方法进行柴油机缸套磨损故障诊断的有效性.
Aiming at fault diagnosis of diesel engine cylinder liner wear, the diesel cylinder block surface vibration monitoring in actual vehicle is conducted, and the diesel cylinder block surface vibration signals in different conditions are analyzed by using empirical mode decomposition(EMD). However, EMD sometimes cannot extract intrinsic mode functions(IMFs) accurately because of the mode mixing. Due to this problem, an improved local wave analysis method based on ensemble empirical mode decomposition(EEMD) is introduced, which is used to alleviate the problem of mode mixing, and the instantaneous frequency and the energy distribution of signals can be extracted by Hilbert boundary-spectrum. The measured results indicate that EEMD method is feasible and effective in fault diagnosis of diesel engine cylinder liner wear.
出处
《大连理工大学学报》
EI
CAS
CSCD
北大核心
2013年第1期71-75,共5页
Journal of Dalian University of Technology
基金
国家自然科学基金资助项目(50805014)
关键词
总体经验模态分解
柴油机
气缸套
磨损
故障诊断
ensemble empirical mode decomposition (EEMD)
diesel engine
cylinder liner
wear
fault diagnosis