基于缸盖振动信号的柴油机多工况气缸压力识别方法研究

A Study on the Cylinder Pressure Identification Method for Diesel Engine Under Multiple Working Conditions Based on Cylinder Head Vibration Signals

对某12150型柴油机进行了缸内燃烧激励的瞬态动力学计算,分析了其缸盖振动的位移、速度和加速度与缸内燃烧特征参数的对应关系。接着在此基础上,对实测振动加速度进行数字积分和平均滤波得到振动位移信号,并利用希尔伯特包络和滑动平均法提取了振动位移的趋势项。再以该趋势项为输入参数构建了Adaboost_BP集成神经网络模型,最后利用此模型对不同工况下的缸内压力进行识别。结果表明:振动位移趋势项与缸内压力的良好对应关系和参数本身的简洁性有效降低了神经网络输入的复杂度,提高了神经网络的训练效率;集成神经网络模型能够准确识别不同工况下的缸内压力,其泛化性和精度均有大幅度提高。

The transient dynamics calculation of in-cylinder combustion excitation in a 12150 diesel engine is carried out and the correlations between the displacement,velocity and acceleration of cylinder head vibration and the characteristic parameters of in-cylinder combustion are analyzed.Then on these bases,with the digital integration and average filtering of the vibration acceleration measured,vibration displacement signals are obtained,and by using Hilbert envelope and moving average method the trend of vibration displacement is extracted,with which as input parameter,Adaboost_BP integrated neural network model is built.Finally based on the model the cylinder pressures in different working conditions are identified.The results show that the good correlation between the trend of vibration displacement and cylinder pressure as well as the brevity of parameters themselves effectively reduce the complexity of neural networks input,and hence improve the training efficiency of neural network,while the integrated neural networks model can accurately identify cylinder pressures under different working conditions,with its generalization and accuracy greatly increased.