基于缸盖振动信号的气缸压力识别方法研究  

Engine Cylinder Pressure Identification Method Based on Cylinder Head Vibration Signals

作　　者：刘建敏[1] 李华莹[1] 乔新勇[1] 李晓磊[1] 史玉鹏[1] 

机构地区：[1]装甲兵工程学院机械工程系,北京100072

出　　处：《内燃机工程》2013年第4期32-37,共6页Chinese Internal Combustion Engine Engineering

基　　金：装备预先研究项目(40402020101)

摘　　要：针对某型12150柴油机燃烧段信号随工况变化进行分析,选择转速800r/min、载荷400N.m时的缸内压力及缸盖振动加速度信号进行时频分析。为了提取缸内最高燃烧压力所激励的缸盖振动加速度信号对气缸压力进行有效的识别,应用小波包去噪方法去除针阀落座及缸内压力高频振荡等高频干扰信号。应用遗传算法优化出BP神经网络最佳的初始权值和阈值,建立了缸盖振动加速度与气缸压力的非线性关系。研究结果表明:与BP神经网络相比,GA-BP神经网络具有更高的精度,识别的气缸压力波形更逼近于实际波形,并且在低转速低负荷条件下具有较强的工况适应性。Combustion stage signals of Model 12150 Diesel Engine were analyzed under different operation conditions. Cylinder head vibration signals and cylinder pressure signals in 800 r/min, 400 N· m were selected to analyze Time-Frequency characteristics. For extracting effectively the cylinder head vibration signals excited by cylinder combustion pressure, the high-frequency interferences such as the injector needle valve seating and combustion pressure high-frequency oscillation,were denoised with wavelet packet analysis method. Optimizing initialized weights and thresholds of the BP neural network by genetic algorithm, nonlinear relation between cylinder head vibration signals and cylinder pressure signals was set up in time domain. Results indicate that compared with BP algorithm, GA-BP algorithm has higher precision and the achieved cylinder pressure curve is more close to the practical, and has stronger adaptability to low speed light load conditions.

关 键 词：内燃机 柴油机 振动信号 小波包去噪 气缸压力 遗传算法 神经网络 

分 类 号：TK428[动力工程及工程热物理—动力机械及工程]