【摘要】 针对在信号特征提取中的噪声抑制问题,提出了一种新的旋转机械振动信号特征提取方法,即时序多相关-经验模式分解方法。通过对原始数据进行时间序列多相关处理,消除了采样序列中零均值噪声信号的影响,克服了在后继频谱分析中由于噪声而导致频谱难于辨识的问题,凸显了原始信号中的特征信号成分。时序多相关分析为后继的谱分析提供了便于处理的前处理数据。应用经验模式分解,能较充分地表现出所需提取的特征信号。仿真和实际某型发动机转轴振动信号特征提取中的分析表明,新方法能成功提取到旋转机械振动信号包含的各个特征信号,证明该方法在旋转机械振动信号特征提取中具有很好的工程应用前景。更多还原

【Abstract】 A novel method named multi-correlation of time series and empirical mode decomposition for suppressing the noise in the feature extraction was developed.It eliminates the effect of zero-average noise in sampling series,get over the difficulty of frequency identification because of strong background noise in the subsequence analysis,protrudes the feature components of original signals,and provides a wonderful former data for spectrum analysis using the multi-correlation of time series.It can sufficiently put up the needed feature signals making use of empirical mode decomposition.The results of simulation analysis and experiments to extract the feature of some engine’s rotating shaft vibration signals validate this method can be used to extract the features of vibration signals of rotating machinery,and prove it has bright future in the engineering applications.更多还原