摘要
气缸压力作为发动机的重要指标,直接反映了发动机燃烧状态的好坏。由于发动机的运行条件复杂,多数条件下为非平稳状态,如何提高发动机在多工况下的缸压识别精度成为了缸压恢复的难点。提出了一种利用振动信号恢复缸压的新方法,以振动信号的最大熵谱密度作为特征,并采用道格拉斯-普克算法对输入输出向量进行了降维处理,利用遗传算法优化的多隐含层BP神经网络有效恢复了多工况下的柴油发动机缸压曲线。经试验测得:经平均化后的缸压曲线峰值最大恢复误差为0. 05 MPa,位置误差最大为0. 6°CA,满足缸压恢复的精度要求。
As an important index of a diesel engine,its cylinder pressure directly reflects its combustion state. Due to complex operating conditions of the engine,it is in a non-stationary state under most conditions. How to improve the cylinder pressure’s recognition accuracy under multiple working conditions becomes the key of cylinder pressure recovery.Here,a new method for cylinder pressure recovery using vibration signals was proposed. Taking the maximum entropy spectral density of a vibration signal as its feature,Douglas-Puke algorithm was adopted to reduce the dimension number of input and output feature vectors. Finally,the BP neural network with multi-hidden layer optimized using the genetic algorithm was used to effectively restore the cylinder pressure curve of the engine under multiple operating conditions. The test results showed that after the average treatment,the maximum restoring error of the cylinder pressure curve’s peak values is 0. 05 MPa and their maximum location error is 0. 6 °CA to meet the accuracy requirement of cylinder pressure recovery.
作者
张帅
曾锐利
ZHANG Shuai;ZENG Ruili(Military Representative Office in Xi＇an,Military Representative Bureau of Munitions,Xi＇an 710000,China;Postgraduate Training Brigade,Military Transportation University,Tianjin 300161,China;Military Vehicle Department,Military Transportation University,Tianjin 300161,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2018年第21期150-157,共8页
Journal of Vibration and Shock
基金
国防预研项目(40407030401)
关键词
柴油发动机缸压
振动信号
最大熵谱
道格拉斯-普克算法
BP神经网络
遗传算法
diesel engine cylinder pressure
vibration signal
maximum entropy spectrum
Douglas-Puke algorithm
BP neural network
genetic algorithm
