基于汽缸压力辨识的发动机失火故障诊断

doi:10.13229/j.cnki.jdxbgxb201703031

摘要/Abstract

摘要： 针对发动机失火故障,提出了一种利用傅里叶级数和L-M算法优化BP神经网络的故障诊断方法。利用AMESim发动机模型进行稳态工况实时仿真,应用L-M算法优化BP神经网络预测节气门开度与发动机汽缸做功频率的关系,同时借助傅里叶级数辨识缸内压力,将由曲轴转速实时映射的汽缸压力模型与所辨识的缸内压力模型进行比较,得出失火诊断结果;并对辨识的压力模型进行相位和频率补偿,提高了诊断精度及诊断方法泛化性。在出现失火故障后再次进行相位和频率补偿,使辨识的模型具有强跟踪性。任取两个节气门开度值进行试验验证,结果表明:本文方法在发动机稳态工况下,无论高转速小负载还是低转速大负载,均可以准确识别出单缸连续失火故障和多缸随机失火故障,验证了本文方法的准确性。

关键词: 自动控制技术, 傅里叶级数, BP神经网络, 发动机失火故障, 发动机汽缸压力, AMESim

Abstract: In order to solve the problem of engine misfire fault, a diagnosis method based on cylinder pressure identified by Fourier Transform (FT) and L-M optimized BP neural network is proposed. The BP neural network is trained by the steady state simulation data from AMESim to obtain the relationship between open value of valve and frequency, and FT is used to identify the cylinder pressure. The misfire failure can be diagnosed by comparison of the FT identified pressure with the pressure mapped from crank speed. By offsetting the phase and frequency of the identified model, the accuracy and generalization of the identified model are improved. When misfire fault occurs, high tracking ability can be obtained by re-offsetting the phase and frequency of the identified model. Two random and independent open values of the valve are used to verify the proposed method, Results show that no matter engine at high speed with light load condition or at low speed with heavy load condition, this method can precisely diagnose the signal-cylinder continuous misfire fault and multi-cylinder random misfire fault.

Key words: automatic control technology, Fourier series, BP neural network, engine misfire fault, engine cylinder pressure, AMESim

中图分类号:

TP29

王德军, 吕志超, 王启明, 张贤达, 王子健. 基于汽缸压力辨识的发动机失火故障诊断[J]. 吉林大学学报(工学版), 2017, 47(3): 917-923.

WANG De-jun, LYU Zhi-chao, WANG Qi-ming, ZHANG Xian-da, WANG Zi-jian. Engine misfire fault diagnosis based on cylinder pressure identification[J]. 吉林大学学报(工学版), 2017, 47(3): 917-923.

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