基于提升小波包变换集成特征提取模型

An Integration Feature Extraction Model Based on Lifting Wavelet Package Transform

为了从发动机缸盖振动信号中快速提取出反应完备状态信息的特征,针对信号特点和提升小波包变换的性质,建立了适于在线提取的缸盖振动信号特征参数体系。总结了提升小波包变换的3个性质—不相关性、可逆性和保序性,利用3个性质将降噪、特征提取和消除波动集成到一次提升小波包分解与重构过程,建立了发动机缸盖振动信号集成特征提取模型,给出了集成消除波动的特征计算公式和特征值标准化公式。通过实例,对集成特征提取模型的工作过程进行了说明。

In order to extract self-contained features which can reflect state information roundly from engine cylinder head vibration signals, a feature parameter system adapting to extract online is built. Characteristics of signals and qualities of lifting wavelet package transform are taken into account in the feature parameter system. Three qualities of lifting wavelet package transform, including irrelevance, reversibility and changeless sequence are concluded. De-noising, feature extraction and eliminating fluctuation are integrated in one decomposition and reconstruction process of lifting wavelet package. The integrated feature extraction model for engine cylinder head vibration signal is built. The calculation formula integrated eliminating fluctuation and standardization formula for feature value are given. The work process of the integrated feature extraction model is illustrated with an example.