摘要
为了解决噪声干扰条件下叶尖定时脉冲信号难以准确提取的问题,提出了一种基于集合经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)的含噪叶尖定时脉冲信号提取方法。采用EEMD对非平稳、非线性的原始含噪信号进行自适应分解,构建多尺度低通滤波器进行降噪,再对降噪后的脉冲信号进行方波整形处理,并提出融合相似度和相关度的最优降噪整形评价指标,用于含噪信号处理效果的评价。基于含噪叶尖定时脉冲信号特点建立数学模型,验证方法的可靠性和适用性。对比降噪整形处理后含噪叶尖定时脉冲信号与原始无噪信号,其相关度、相似度及综合评价指标分别超过97%、89%及92%,结果表明:该方法能够准确提取噪声干扰下叶尖定时脉冲信号,有利于叶尖定时技术的发展与推广应用。
It is difficult to accurately extract the blade tip-timing pulse signals in the environments with noise interference. In this paper, a noised blade tip-timing pulse signal extracting method based on ensemble empirical mode decomposition (EEMD) was proposed to solve this problem. In this method, non-stationary and non-linear original noised signals are adaptively decomposed by means of EEMD, and a multi-scale low pass filter is constructed for denoising. Secondly, the denoised pulse signals are processed by means of square wave shaping. And thirdly, optimal denoising and shaping evaluation indexes of fusion similarity and correlation are put forward to evaluate the processing results of noised signals. Then, based on the characteristics of noised blade tip-timing pulse signals, a mathematical model was established to verify reliability and applicability of this newly developed method. Finally, the noised blade tip-timing pulse signals after denoising and shaping were compared with the original noiseless signals, and their correlation, similarity and comprehensive evaluation index are above 97%, 89% and 92%, respectively. It is indicated that this EEMD based noised blade tip-timing pulse signal extracting method can accurately extract the blade tip-timing pulse signals in the environments with noise interference, and be beneficial to the development, popularization and application of blade tip-timing technology.
作者
张继旺
张来斌
段礼祥
王耀楠
ZHANG Jiwang;ZHANG Laibin;Duan Lixiang;WANG Yaonan(China Special Equipment Inspection and Research Institute;College of Mechanical and Transportation Engineering, China University of Petroleum(Beijing))
出处
《油气储运》
CAS
北大核心
2019年第6期685-691,共7页
Oil & Gas Storage and Transportation
基金
国家重点研发计划资助项目“烟机叶片运行状态监测技术研究”,2016YFF0203302
国家自然科学基金资助项目“基于迁移学习的往复压缩机故障诊断机制及预测预警模型研究”,51674277
关键词
叶尖定时脉冲信号
集合经验模态分解
EEMD
方波整形
降噪
信号处理
blade tip-timing pulse signal
ensemble empirical mode decomposition
EEMD
square wave shaping
denoising
signal processing
