摘要
提取出水电机组振动信号中故障特征和微弱征兆信号,可以更好地了解机组的运行状态和故障发展趋势,但由于水电机组多源振动信号的相互混叠,信号呈现出非线性、非平稳性,故障特征信号提取是该领域的一个难题。为此该文提出了一种基于独立分量分析(independent component analysis,ICA)和经验模态分解(empirical mode decomposition,EMD)的特征提取新方法(ICA-EMD)。首先,用ICA将多通道振动信号分离成为统计独立分量;然后,对这些统计独立分量做自相关分析,消除环境噪声的影响;最后,对消噪后的所有统计独立分量统计逐一进行EMD分解,并将各个统计独立分量内蕴的同频本征模态函数进行累加重构,最终提取出能表征机组故障的本征模态函数。仿真信号和实测信号的试验验证表明,与其他方法相比,该方法在提取故障早期信号、微弱信号和突变信号方面更具优越性和有效性,提取结果更能满足实际工程应用需求。
In order to understand the operating status and the fault development trend of hydroelectric generating units, extracting the failure feature or incipient symptom from vibration signals is foundational. However, vibration signals of hydroelectric generating units presents non-linear, aliasing and non-stable character, its feature extraction is a challenging problem in this field. Thus, a new feature extraction method （ICA-EMD） of vibration signals for hydroelectric generator units is proposed by combining independent component analysis （ICA） with empirical mode decomposition （EMD）. Firstly, the signals form multi vibration sensors were separated into statistics independent component by ICA. Then, each of statistics independent components was analyzed by using the method of autocorrelation analysis to eliminate the interference of the background noise. Finally, all of the statistics independent components were decomposed by using EMD, and the fault feature signals were obtained by accumulating and reconstructing all the co-channel components. The results of simulations experiment and an application example show that this method can extract efficiently incipient symptom, weak signals, transient signals and other fault feature signals. Compared with other methods, this method i.s an effective way to practical projects.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2013年第29期95-101,14,共7页
Proceedings of the CSEE
基金
国家自然科学基金项目(51079057
51109088)
高等学校博士学科点专项科研基金(20100142110012)~~
关键词
特征提取
独立分量分析(ICA)
经验模态分解(EMD)
水电机组
故障诊断
feature extraction
independent componentanalysis （ICA）
-empirical mode decomposition （EMD）
hydroelectric generating unit
fault diagnosis
