基于稀疏表达特征提取的发动机爆震状态检测  

Engine Knock Detection Based on Sparse Representation Feature Extraction

作　　者：沈鹏飞 毕凤荣[1] 马小强[2] 李鑫 汤代杰 杨晓[1] SHEN Pengfei;BI Fengrong;MA Xiaoqiang;LI Xin;TANG Daijie;YANG Xiao(State Key Laboratory of Engines,Tianjin University Tianjin,300072,China;Tianjin Internal Combustion Engine Research Institute Tianjin,300072,China)

机构地区：[1]天津大学内燃机燃烧学国家重点实验室,天津300072 [2]天津内燃机研究所,天津300072

出　　处：《振动．测试与诊断》2021年第5期926-932,1034,共8页Journal of Vibration,Measurement & Diagnosis

基　　金：内燃机可靠性国家重点实验室开放课题资助项目(WCDL-GH-2021-0017)。

摘　　要：研究了利用发动机缸体振动信号进行爆震检测和强度评价的方法,提出了一种基于广义正交匹配追踪的改进K-均值奇异值分解(K-means singular value decomposition,简称K-SVD)信号处理方法,将稀疏表达理论引入了发动机爆震特征识别领域。首先,对缸体振动信号进行稀疏分解,得到涵盖爆震特征的稀疏字典以及针对单个信号的稀疏系数;然后,计算重构信号的四阶累积量的自然对数,提出了一种爆震强度评价指标。计算结果表明,该方法对于混有强烈背景噪声的缸体振动信号表现出了良好的降噪和特征提取能力,且提高了运算效率,能够准确区分强烈爆震、轻微爆震和正常燃烧3种状态,证明了该方法在发动机爆震识别领域的应用价值。The research focuses on engine knock detection and intensity evaluation using vibration signal. Based on the generalized orthogonal matching pursuit,a K-mean singular value decomposition(K-SVD)method,classified as sparse representation theory,is introduced into the engine knock feature extraction. The cylinder block vibration signal is sparse decomposed to the sparse dictionary covering the knock characteristics and the sparse coefficient for each single signal. On the basis of the above,a knock intensity evaluation index is proposed by using the natural logarithm of the fourth-order cumulant of the reconstructed signal. This method shows good noise reduction ability and feature extraction ability for cylinder vibration signal mixed with strong background noise.The results show that the heavy knock,light knock and normal combustion can be effectively distinguished by using this method. It proves the application value of this method in the field of engine knock identification.

关 键 词：稀疏表达 内燃机 爆震特征 K-均值奇异值分解 广义正交匹配追踪 四阶累积量 

分 类 号：TK411[动力工程及工程热物理—动力机械及工程] TH17[机械工程—机械制造及自动化]