基于小波去噪和卷积神经网络的发动机爆震识别  

Engine knock recognition based on wavelet domains denoising and convolutional neural network

作　　者：胡春明[1,2] 刘铮[1,2] 刘娜 宋玺娟[1] 杜春媛[1] HU Chunming;LIU Zheng;LIU Na;SONG Xijuan;DU Chunyuan(Internal Combustion Engine Research Institute,Tianjin University,Tianjin 300072,China;School of Mechanical Engineering,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学内燃机研究所,天津300072 [2]天津大学机械工程学院,天津300072

出　　处：《航空动力学报》2024年第7期151-160,共10页Journal of Aerospace Power

基　　金：国家自然科学基金(51476112)。

摘　　要：在活塞式航空煤油发动机上进行爆震试验研究,首先使用小波去噪对发动机缸压信号进行噪声提取,然后对0°~45°曲轴转角内的噪声信号进行快速傅里叶变换将一维时域噪声信号展开成二维时频域特征图,最后将特征图输入到训练好的卷积神经网络(convolutional neural networks,CNN)中进行爆震识别.验证结果表明:轻微和严重爆震都会在10°~30°曲轴转角内产生幅值较大噪声信号,与无爆震循环的时频域特征图有明显区别;在爆震特征提取上小波去噪要优于带通滤波,在爆震特征识别上CNN方法要优于支持向量机(support vector machine,SVM)方法;小波去噪和CNN结合的爆震识别方法对发动机4种不同运行工况的爆震识别准确率都能达到91%以上;小波去噪结合CNN方法对爆震循环的查准率为83.16%,查全率高达98.79%,能够准确地识别出发动机的爆震循环.Based on the method of wavelet domain denoising,the noise signals from in-cylinder pressure were extracted,at crank angle of 0°-45°,fast Fourier transform was used for simultaneous analysis of the noise signal in the time and frequency domains,then the feature map was outputted.The map was inputted into convolutional neural network(CNN)for identifying different features in order to distinguish non-knock and knock.The knock test was conducted on a direct injection engine fueled with aviation kerosene.The result revealed that:the time-frequency map was significantly different between knock and non-knock,because slight knocking and severe knocking both produced large-amplitude noise signals within crank angle of 10°-30°.Wavelet denoising was better than bandpass filtering in knocking feature extraction,while CNN was better than Support Vector Machine(SVM)in knocking feature recognition;under four different operating conditions,the knock recognition accuracy was all over 91%by wavelet domain denoising combining with CNN method;the precision and recall of the knock were 83.16%and 98.79%,respectively.

关 键 词：爆震识别 活塞式航空煤油发动机 小波分析 小波去噪 深度学习 卷积神经网络 

分 类 号：V234.1[航空宇航科学与技术—航空宇航推进理论与工程]