基于改进段角加速度和神经网络的柴油机失火诊断研究
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摘要
针对失火故障中存在的高速轻载诊断困难,失火程度无法判别的问题,通过对比分析正常状态与失火情况下瞬时转速的特征,发现缩短段角加速度段长度,能够有效提升特征对失火故障的敏感度,同时,用神经网络方法代替阈值规则,能够很好地利用各缸特征值间的联系诊断失火。基于此,提出一种改进段角加速度和神经网络相结合的失火故障诊断方法。该方法能够实现对全转速范围单缸完全失火的诊断,且利用二级诊断的方式可以对失火程度进行有效判别,在高速轻载工况依旧具有很好的准确率。同时,提出的方法在学习阶段所需数据量小,适用于发动机失火故障的在线诊断。
This paper focus on the diagnosis problem of the misfire and the misfire degree caused at high speed and low loads,compare and analyse the transient speed characteristics under normal and misfire conditions,and find it can effectively improve characteristics sensitivity of engine misfire by shortening the segment length of angular acceleration speed.According to the analysis on the misfire fault diagnosis,it is found that the threshold value method is replaced with the neural network method can effectively diagnose the misfire by employing the relationship between different cylinder characteristic value.Based on the above,a improved misfire diagnosis method of combination of improved segment angular acceleration with the neural network is proposed finally.This method can carry out accurate single cylinder misfire diagnosis over the full speed range,and can determine the misfire degree effectively with the second level diagnosis.And the new approach has a good accuracy rate of misfire fault diagnosis.Meanwhile,it requires less data in the network learning phase and is easy to grasp,which is suitable for on-line diagnosis of engine misfire faults.
This paper focus on the diagnosis problem of the misfire and the misfire degree caused at high speed and low loads,compare and analyse the transient speed characteristics under normal and misfire conditions,and find it can effectively improve characteristics sensitivity of engine misfire by shortening the segment length of angular acceleration speed.According to the analysis on the misfire fault diagnosis,it is found that the threshold value method is replaced with the neural network method can effectively diagnose the misfire by employing the relationship between different cylinder characteristic value.Based on the above,a improved misfire diagnosis method of combination of improved segment angular acceleration with the neural network is proposed finally.This method can carry out accurate single cylinder misfire diagnosis over the full speed range,and can determine the misfire degree effectively with the second level diagnosis.And the new approach has a good accuracy rate of misfire fault diagnosis.Meanwhile,it requires less data in the network learning phase and is easy to grasp,which is suitable for on-line diagnosis of engine misfire faults.
引文
[1]SOOD A K,FAHS A A,HENEIN N A.Engine fault analysis:partⅡparameter estimation approach[J].IEEETransactions on Industrial Electronics,1985,(4):301-307.
[2]贾继德,葛同民,杨万成,等.基于非平稳周期循环特征增强的柴油机失火故障诊断研究[J].内燃机工程,2013,34(1):67-70.JIA J D,GE T M,YANG W C,et al.Research on diagnosis of diesel engine misfire fault based on non-stationary cycle feature enhancement[J].Chinese Internal Combustion Engine Engineering,2013,34(1):67-70.
[3]乔新勇,刘建敏,张小明.基于神经网络信息融合的发动机失火故障诊断[J].内燃机工程,2009,30(1):74-79.QIAO X Y,LIU J M,ZHANG X M.Method for diagnosing misfiring fault of engine based on neural network data fusion[J].Chinese Internal Combustion Engine Engineering,2009,30(1):74-79.
[4]任刚,贾继德,梅检民,等.基于阶比跟踪与VMD的瞬变工况发动机故障特征提取[J].内燃机工程,2018,39(4):54-60.REN G,JIA J D,MEI J M,et al.Extraction of features of diesel engine faults under transient operations based on order tracking and variational mode eecomposition[J].Chinese Internal Combustion Engine Engineering,2018,39(4):54-60.
[5]樊新海,安钢,张传清,等.基于排气噪声EMD的柴油机失火故障诊断[J].内燃机工程,2010,31(1):78-81.FAN X H,AN G,ZHANG C Q,et al.Misfire fault diagnosis for diesel engine based on EMD of exhaust noise[J].Chinese Internal Combustion Engine Engineering,2010,31(1):78-81.
[6]樊新海,姚炽伟,曾兴祥,等.基于排气噪声局域均值分解的失火故障诊断[J].内燃机工程,2013,34(4):38-41.FAN X H,YAO C W,ZENG X X,et al.Misfire fault diagnosis based on local mean decomposition of exhaust noise[J].Chinese Internal Combustion Engine Engineering,2013,34(4):38-41.
[7]康葳,乔新勇,安钢.基于统计模拟的柴油机失火故障的诊断方法[J].内燃机工程,2004,25(5):66-68.KANG W,QIAO X Y,AN G.Method of diagnosis diesel engine misfire fault based on statistical simulation[J].Chinese Internal Combustion Engine Engineering,2004,25(5):66-68.
[8]梁锋,杨林,赫强,等.电控柴油机的在线失火诊断策略研究[J].内燃机学报,2004,22(4):332-336.LIANG F,YANG L,HE Q,et al.Study of on-board misfires diagnosing strategy of electronically controlled engine[J].Transactions of CSICE,2004,22(4):332-336.
[9]PONTI F.Instantaneous engine speed time-frequency analysis for on-board misfire detection and cylinder isolation in a V12high performance engine[J].Journal of Engineering for Gas Turbines&Power,2002,130(1):547-556.
[10]WANG H,SUN W,LIU Y.Study on misfire diagnostic strategy of on-board diagnostics system on LPG passenger car[C]//Wuhan:Power and Energy Engineering Conference(APPEEC)2011Asia-Pacific,2011:5748876.
[11]CHEN J,RANDALL R B.Improved automated diagnosis of misfire in internal combustion engines based on simulation models[J].Mechanical Systems&Signal Processing,2015,65:58-83.
[12]PONTI F.Instantaneous engine speed analysis for cylinder isolation in multiple misfire events[C]//Pennsylvania:ASME2003Internal Combustion Engine and Rail Transportation Divisions Fall Technical Conference,2003:2003-0725.
[13]任卫军,贺昱曜,张卫钢.基于曲轴段加速度的内燃机失火故障在线诊断[J].汽车工程,2010,32(4):339-342.REN W J,HE Y Y,ZHANG W G.On-line diagnosis on misfire fault of internal combustion engine based on crankshaft segment acceleration[J].Automotive Engineering,2010,32(4):339-342.
[14]PLAPP G,KLENK M,MOSER W.Methods of on-board misfire detection[C/OL].SAE Paper,1990,900232.[1990-02-01].http://papers.sae.org/900232.
[15]NAREID H,LIGHTOWLER N.Detection of engine misfire events using an artificial neural network[C/OL].SAE Paper,2004,2004-01-1363.(2004-03-08)[2018-02-12].http://papers.sae.org/2004-01-1363.
[16]ALI A,MAGNOR O,SCHULTALBERS M.Misfire detection using a neural network based pattern recognition technique[C]//Rakistan:International Conference on Electrical Engineering,2007:4287338.
[2]贾继德,葛同民,杨万成,等.基于非平稳周期循环特征增强的柴油机失火故障诊断研究[J].内燃机工程,2013,34(1):67-70.JIA J D,GE T M,YANG W C,et al.Research on diagnosis of diesel engine misfire fault based on non-stationary cycle feature enhancement[J].Chinese Internal Combustion Engine Engineering,2013,34(1):67-70.
[3]乔新勇,刘建敏,张小明.基于神经网络信息融合的发动机失火故障诊断[J].内燃机工程,2009,30(1):74-79.QIAO X Y,LIU J M,ZHANG X M.Method for diagnosing misfiring fault of engine based on neural network data fusion[J].Chinese Internal Combustion Engine Engineering,2009,30(1):74-79.
[4]任刚,贾继德,梅检民,等.基于阶比跟踪与VMD的瞬变工况发动机故障特征提取[J].内燃机工程,2018,39(4):54-60.REN G,JIA J D,MEI J M,et al.Extraction of features of diesel engine faults under transient operations based on order tracking and variational mode eecomposition[J].Chinese Internal Combustion Engine Engineering,2018,39(4):54-60.
[5]樊新海,安钢,张传清,等.基于排气噪声EMD的柴油机失火故障诊断[J].内燃机工程,2010,31(1):78-81.FAN X H,AN G,ZHANG C Q,et al.Misfire fault diagnosis for diesel engine based on EMD of exhaust noise[J].Chinese Internal Combustion Engine Engineering,2010,31(1):78-81.
[6]樊新海,姚炽伟,曾兴祥,等.基于排气噪声局域均值分解的失火故障诊断[J].内燃机工程,2013,34(4):38-41.FAN X H,YAO C W,ZENG X X,et al.Misfire fault diagnosis based on local mean decomposition of exhaust noise[J].Chinese Internal Combustion Engine Engineering,2013,34(4):38-41.
[7]康葳,乔新勇,安钢.基于统计模拟的柴油机失火故障的诊断方法[J].内燃机工程,2004,25(5):66-68.KANG W,QIAO X Y,AN G.Method of diagnosis diesel engine misfire fault based on statistical simulation[J].Chinese Internal Combustion Engine Engineering,2004,25(5):66-68.
[8]梁锋,杨林,赫强,等.电控柴油机的在线失火诊断策略研究[J].内燃机学报,2004,22(4):332-336.LIANG F,YANG L,HE Q,et al.Study of on-board misfires diagnosing strategy of electronically controlled engine[J].Transactions of CSICE,2004,22(4):332-336.
[9]PONTI F.Instantaneous engine speed time-frequency analysis for on-board misfire detection and cylinder isolation in a V12high performance engine[J].Journal of Engineering for Gas Turbines&Power,2002,130(1):547-556.
[10]WANG H,SUN W,LIU Y.Study on misfire diagnostic strategy of on-board diagnostics system on LPG passenger car[C]//Wuhan:Power and Energy Engineering Conference(APPEEC)2011Asia-Pacific,2011:5748876.
[11]CHEN J,RANDALL R B.Improved automated diagnosis of misfire in internal combustion engines based on simulation models[J].Mechanical Systems&Signal Processing,2015,65:58-83.
[12]PONTI F.Instantaneous engine speed analysis for cylinder isolation in multiple misfire events[C]//Pennsylvania:ASME2003Internal Combustion Engine and Rail Transportation Divisions Fall Technical Conference,2003:2003-0725.
[13]任卫军,贺昱曜,张卫钢.基于曲轴段加速度的内燃机失火故障在线诊断[J].汽车工程,2010,32(4):339-342.REN W J,HE Y Y,ZHANG W G.On-line diagnosis on misfire fault of internal combustion engine based on crankshaft segment acceleration[J].Automotive Engineering,2010,32(4):339-342.
[14]PLAPP G,KLENK M,MOSER W.Methods of on-board misfire detection[C/OL].SAE Paper,1990,900232.[1990-02-01].http://papers.sae.org/900232.
[15]NAREID H,LIGHTOWLER N.Detection of engine misfire events using an artificial neural network[C/OL].SAE Paper,2004,2004-01-1363.(2004-03-08)[2018-02-12].http://papers.sae.org/2004-01-1363.
[16]ALI A,MAGNOR O,SCHULTALBERS M.Misfire detection using a neural network based pattern recognition technique[C]//Rakistan:International Conference on Electrical Engineering,2007:4287338.