基于汽缸压力辨识的发动机失火故障诊断
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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。
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 multicylinder random misfire fault.
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 multicylinder random misfire fault.
引文
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[2]Rizvi M A,Bhatti A I,Butt Q R.Hybrid model of the gasoline engine for misfire detection[J].IEEE Transactions on Industrial Electronics,2011,58(8):3680-3692.
[3]Jung D,Frisk E,Krysander M.A flywheel error compensation algorithm for engine misfire detection[J].Control Engineering Practice,2016,47:37-47.
[4]杨梅,张振文,孙宏强,等.小波分析与傅里叶分析的比较及其在故障诊断中的应用[J].中国测试技术,2005,31(2):58-60.Yang Mei,Zhang Zhen-wen,Sun Hong-qiang,et al.The comparison of wavelet and Fourier analysis and their application to fault diagnosis[J].China Measurement Technology,2005,31(2):58-60.
[5]Wu Z J,Lee A.Misfire detection using a dynamic neural network with output feedback[J].SAE Paper,980515.
[6]胡重庆,李艾华,朱春红,等.基于瞬时转速统计分析的内燃机失火检测[J].内燃机学报,2009,27(5):446-451.Hu Chong-qing,Li Ai-hua,Zhu Chun-hong,et al.Misfire detection based on statistical analysis of instantaneous rational speed[J].Transactions of CSICE,2009,27(5):446-451.
[7]Wang Yin-hui,Huang Kai-sheng,Lin Zhi-hua,et al.Advanced gasoline engine misfire diagnostic method based on crankshaft speed multiple filtering[DB/OL].[2016-03-27].http:∥ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5776855.
[8]王银辉,黄开胜,林志华,等.发动机多缸随机失火诊断算法研究[J].内燃机工程,2012,33(1):18-26.Wang Yin-hui,Huang Kai-sheng,Lin Zhi-hua,et al.Study of engine multi-cylinder random misfire detection[J].Chinese Internal Combustion Engine Engineering,2012,33(1):18-26.
[9]褚金奎,孙建伟.基于傅里叶级数理论的连杆机构轨迹综合方法[J].机械工程学报,2010,46(13):31-41.Chu Jin-kui,Sun Jian-wei.Unified approach to synthesis of coupler curves of linkage by Fourier series[J].Journal of Mechanical Engineering,2010,46(13):31-41.
[10]胡杰,颜伏伍.基于BP神经网络的汽油机失火故障诊断方法的研究[J].汽车工程,2011,33(2):101-105.Hu Jie,Yan Fu-wu.A research on the misfire diagnosis method of gasoline engine based on BP neural network[J].Automotive Engineering,2011,33(2):101-105.
[11]沈花玉,王兆霞,高成耀,等.BP神经网络隐含层单元数的确定[J].天津理工大学学报,2008,24(5):13-15.Shen Hua-yu,Wang Zhao-xia,Gao Cheng-yao,et al.Determining the number of BP neural network hidden layer units[J].Journal of Tianjin University of Technology,2008,24(5):13-15.
[12]奥本海默.信号与系统[M].刘树棠译.2版.西安:西安交通大学出版社,2005.
[13]王赟松,褚福磊,郭丹.基于二阶滑模技术的内燃机气缸压力的估计[J].内燃机学报,2004,22(6):555-561.Wang Yun-song,Chu Fu-lei,Guo Dan.Cylinder pressure estimation for engines using second-order sliding modes[J].Transactions of CSICE,2004,22(6):555-561.
[14]于明进,程勇,吴波,等.试验确定发动机转动惯量的一种新方法[J].农业机械学报,2005,36(7):34-37.Yu Ming-jin,Cheng Yong,Wu Bo,et al.New method for estimating the inertia moment of an engine[J].Transactions of the Chinese Society for Agricultural Machinery,2005,36(7):34-37.