摘要
研究了小波包变换提取爆震特征的方法,利用加速度传感器信号进行处理分析.由参数化功率谱密度估计,确定由爆震引起的共振频率,截取爆震能量集中区间较短的数据段,建立AR模型,然后利用Burg算法估计爆震的特征频率范围,确定小波包变换的分解级数,保留与爆震特征频率相对应的小波包变换子空间信息,合理取舍其它子带系数,使重构后的信号能有效地表征爆震特征.实测信号分析表明,在轻微爆震检测方面,该方法优于传统的滤波方法和基础小波分析方法.
Engine knocking feature extraction using wavelet package transform is studied in this paper.Vibration signals acquired by an accelerometer were analyzed.The resonance frequency generated by knocking component is determined by the estimation of power spectrum density（PSD）.An autoregressive（AR）model is made for the narrow data segment containing the knock energy,and the resonance frequency is estimated with Burg algorithm.The extent of wavelet packet decomposition is decided according to the result.The coefficients of sub-bands are chosen with a method proposed,so that knock feature is obtained,while noise is removed reasonably.Analysis in real vibration signals indicates that the proposed method is better than the conventional filtering method and basic wavelet analyzing method for the light knocking detection.
出处
《内燃机学报》
EI
CAS
CSCD
北大核心
2012年第2期161-165,共5页
Transactions of Csice
基金
国家自然科学基金资助项目(50676039)
关键词
发动机
小波包变换
特征提取
爆震检测
engine
wavelet packet transform
feature extraction
knock detection
