柴油发动机高压共轨系统智能诊断的研究
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摘要
随着科学技术的飞速发展,世界各国政府对汽车领域节能与环保的要求不断提高,高压共轨技术正是为了适应这一潮流和趋势而发展起来的新技术。在我国采用高压共轨柴油发动机的机动车数量大幅增长,高压共轨系统的故障诊断问题就摆在了人们面前。但是由于我国还没有掌握高压共轨系统的故障诊断技术,故障车辆必须依靠国外指定的少数维修站来高价检测,因此这极大的影响了我国汽车产业对节能环保技术的推广与应用。成为影响我国汽车产业发展的瓶颈。研发具有自主知识产权的高压共轨故障诊断系统迫在眉睫。
在高压共轨故障诊断系统的研发中,需要解决高压共轨系统高压压力的控制与故障诊断的问题。
本文在研究现有控制算法的基础上,提出了三种压力控制算法,分别为基于PID的压力控制算法、基于卡尔曼滤波与PID的压力控制算法、基于BP神经网络的压力控制算法。通过试验分析表明,以上三种压力控制算法都可以较好的完成高压共轨系统的高压压力控制,得到较高的压力控制精度,完全可以满足在高压共轨智能诊断系统中压力控制的需要。
在高压共轨系统的故障诊断方面,本文分别提出了基于阈值判别的故障诊断算法与基于BP神经网络的高压共轨系统故障诊断算法。由于BP神经网络具有良好的非线性映射特性,非常适合用于复杂系统的故障诊断。通过试验分析表明,基于BP神经网络的高压共轨系统的故障诊断算法可以很好的完成对高压共轨系统中的高压压力、系统自带传感器以及低压泵的智能故障诊断,其诊断的正确率可以达到99.89%以上,完全可以满足汽车产业对燃油发动机高压共轨系统故障诊断的需要。
As the rapid development of science and technology, all countries have raised the standard of energy saving and environment protection, which makes high pressure common rail system the perfect technology for satisfying all these. In China, number of vehicles using high pressure common rail engines is increasing remarkably, while it comes up with problem of diagnosis of the system. However, China has no command of diagnosis technology of high pressure common rail engines, therefore, designated reparation stations are needed and the price of these stations is very high, which affects the development and implementation of this new technology. It has became a threshold in Chinese automobile industry. It is urgent to develop a self-designed diagnosis of high pressure common rail engine system.
In the design of diagnosis of high pressure common rail engine system, the control and intelligent diagnosis of high pressure needs to be tackled.
Based on the existing control algorithms, this paper presents three pressure control algorithms, namely, pressure control algorithm based on PID, pressure control algorithm based on Kalman filter and PID, pressure control algorithm based on BP neural networks. Experimental results show that these three algorithms can control the pressure of the high pressure common rail system, to obtain higher precise of the pressure. It is proved that they can all satisfy the application of diagnosis of high pressure common rail engine system.
In the diagnosis, this paper presents two algorithms for intelligent diagnosis of high pressure common rail engine system based on thresholds and based on BP neural networks. Because of the great nonlinear projection ability, it is fit for complicated diagnosis. Experimental results show that this algorithm can better diagnosis the high pressure in common rail system, sensors and low-pressure. The preciseness can be higher than 99.89%, which is satisfied in diagnosis of high pressure common rail engine system.
在高压共轨故障诊断系统的研发中,需要解决高压共轨系统高压压力的控制与故障诊断的问题。
本文在研究现有控制算法的基础上,提出了三种压力控制算法,分别为基于PID的压力控制算法、基于卡尔曼滤波与PID的压力控制算法、基于BP神经网络的压力控制算法。通过试验分析表明,以上三种压力控制算法都可以较好的完成高压共轨系统的高压压力控制,得到较高的压力控制精度,完全可以满足在高压共轨智能诊断系统中压力控制的需要。
在高压共轨系统的故障诊断方面,本文分别提出了基于阈值判别的故障诊断算法与基于BP神经网络的高压共轨系统故障诊断算法。由于BP神经网络具有良好的非线性映射特性,非常适合用于复杂系统的故障诊断。通过试验分析表明,基于BP神经网络的高压共轨系统的故障诊断算法可以很好的完成对高压共轨系统中的高压压力、系统自带传感器以及低压泵的智能故障诊断,其诊断的正确率可以达到99.89%以上,完全可以满足汽车产业对燃油发动机高压共轨系统故障诊断的需要。
As the rapid development of science and technology, all countries have raised the standard of energy saving and environment protection, which makes high pressure common rail system the perfect technology for satisfying all these. In China, number of vehicles using high pressure common rail engines is increasing remarkably, while it comes up with problem of diagnosis of the system. However, China has no command of diagnosis technology of high pressure common rail engines, therefore, designated reparation stations are needed and the price of these stations is very high, which affects the development and implementation of this new technology. It has became a threshold in Chinese automobile industry. It is urgent to develop a self-designed diagnosis of high pressure common rail engine system.
In the design of diagnosis of high pressure common rail engine system, the control and intelligent diagnosis of high pressure needs to be tackled.
Based on the existing control algorithms, this paper presents three pressure control algorithms, namely, pressure control algorithm based on PID, pressure control algorithm based on Kalman filter and PID, pressure control algorithm based on BP neural networks. Experimental results show that these three algorithms can control the pressure of the high pressure common rail system, to obtain higher precise of the pressure. It is proved that they can all satisfy the application of diagnosis of high pressure common rail engine system.
In the diagnosis, this paper presents two algorithms for intelligent diagnosis of high pressure common rail engine system based on thresholds and based on BP neural networks. Because of the great nonlinear projection ability, it is fit for complicated diagnosis. Experimental results show that this algorithm can better diagnosis the high pressure in common rail system, sensors and low-pressure. The preciseness can be higher than 99.89%, which is satisfied in diagnosis of high pressure common rail engine system.
引文
[1]臧刚权,杨文位.柴油机故障诊断技术方法综述[J].农机机械化与电气化,2006,6:17-18.
[2]李长河.柴油机共轨喷射系统的发展及关键技术[J].内蒙古民族大学学报,2003,18(2):138-141.
[3]李绍安.国内外共轨系统的开发进展和所面临的问题[J].内燃机燃油喷射和控制,1999(4):5-8.
[4]梁峰,杨林,谭文春等.高压共轨式电控柴油机故障诊断策略仿真研究[J].内燃机学报,2004,22(6):532-537.
[5]徐权奎,祝轲卿,陈自强等.高压共轨柴油机喷油器电磁阀故障诊断系统设计[J].内燃机工程,2007,28(4):69-74.
[6]梁锋,肖文雍,谭文春等.高压共轨式电控柴油机电子油门故障自诊断策略研究[J].汽车技术,2003,7:18-22.
[7]蒋开正,毛俊.电控高压共轨柴油喷射系统故障诊断与维护[J].内燃机,2008,4(2):55-57.
[8]卢兴艳.基于图像处理的高压共轨喷油器故障诊断研究[D].大连:大连理工大学,2008.
[9]虞和济.故障诊断的基本原理[M].北京:冶金工业出版社,1989.
[10]鄂加强.智能故障诊断及其应用[M].湖南:湖南大学出版社,2006.
[11]黄泉水,江国和,肖建昆.基于AR模型和神经网络的柴油机故障诊断[J].噪声与振动控制,2008,3:60-63,91.
[12]FRANK P M.Analytical and qualitative model-based fault diagnosis—a survey and some new results[J].European Journal of Control,1996,2(1):6-28.
[13]Bernard F.On the properties of reduced order Kalman filters[J].IEEE Transactions on Automatic Control,1989,34(3):321-324.
[14]黄显林,卢鸿谦,王宇飞.组合导航非等间隔联合滤波[J].中国惯性技术学报,2002,10(3):1-7.
[15]Julier S J,Uhlmann J K.Unscented filtering and nonlinear estimation[J].Proceedings of the IEEE,2004,92(3):401-421.
[16]黄晓雷,牛申格.扩展卡尔曼滤波在无人机航迹控制系统中的研究应用[J].航空兵器,2008(2):11-13.
[17]王宏禹.随机数字信号处理[M].北京:科学出版社,1988.
[18]Gao Weibing.Variable Structure Control:A Survey[J].IEEE Transactions on Industrial Electronica,1993,40(1):2-22.
[19]Yang C D,Chen HY.Nonlinear Robust Guidance Law for Homing Missiles[J].Journal of Guidance,Control and Dynamics,1998,21(6):882-890.
[20]杨行峻,郑君里.人工神经网络[M].北京:高等教育出版社,1992.
[21]Simon H.Neural Networks A Comprehensive Foundation[M].北京:清华大学出版社,1998.
[22]周志华,曹存根.神经网络及其应用[M].北京:清华大学出版社,2004.
[23]Remelhart.D.E,et al.Learning internal representations by back-propagating errors[J].Nature,1986:533-536.
[24]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2000.
[25]焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社,1990.
[26]HAYKIN S著.神经网络原理[M].叶世伟,史忠植,译.北京:机械工业出版社,2003.
[27]KWONG C K,BAI H.A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment[J].Journal of Intelligent Manufacturing,2002,13(5):367-377.
[28]BAO J,ZHAO J Y,ZHOU H Y.Study on method of curve simulation based on BP network[J].Computer Engineering and Design,2005,26(7):840-841.
[29]虞和济,陈长征,张省,等.基于神经网络的智能诊断[M].北京:冶金工业出版社,2000.
[30]周文华.高压共轨电控喷油系统控制算法研究[J].内燃机工程,2002,23(4):30-33.
[31]刘兴堂.应用自适应控制[M].陕西:西北工业大学出版社,2003.
[32]Richard C.Dorf,Rebert H.Bi sshop著.现代控制系统[M].赵千川,冯妹,译.北京:清华大学出版社,2008.
[33]Kreisselmeier G.Adaptive control via Adaptive Observer and Asymptotic Feedback Matrix Synthesis[J].IEEE Transactions on Automatic Control,1980,25:717-722.
[34]Narendra K S,Valavani L S.Direct and Indirect Model Reference Adaptive Control[J].Automatic,1979,15(6):653-664.
[35]Lin Y,Narendra K S.A New Error Model for Adaptive System[J].IEEE Transactions on Automatic Control,1980,25(3):585-587.
[36]李慧光,武波,杨晨影.Delta算子控制及鲁棒控制理论基础一统——连续域、离散域的控制理论[M].北京:国防工业出版社,2005.
[37]张瑞金,吴捷.基于Delta算子的自适应信号处理[J].系统工程与电子技术,2001,5:4-7.
[38]FAN H,DE P.High speed adaptive signal processing using the Delta operator[J].Digital Signal Processing,A Review Journal,2001,11(1):3-34.
[39]AGARWAL R C,BURROWS C S.New recursive digital filter structures having low sensitivity and round-off noise[J].IEEE Transactions on Circuits and Systems,1975,22(2):921-927.
[40]GOODWIN G C,LEAL R L,MIDDLETON R H.Rapprochement between continuous and discrete model reference adaptive control[J].Automatic,1986,22(2):199-207.
[41]李慧光,张尚斌,王墨琦.基于前向差分Delta算子的LMS自适应滤波算法[J].数据采集与处理,2000,24(2):189-192.
[42]宋雪洁,张端金,王香丽.基于Delta算子的SVD—LMS算法[J].现代电子技术,2006,11:79-80,83.
[43]Stepaniak M J,et al.MMAE-Based Control Redistribution Applied to the VISTA F-16.IEEE Transactions on Aerospace and Electronic Systems.1998,34(4):1249-1260.
[44]吴振顺.自适应控制理论与应用[M].黑龙江:哈尔滨工业大学出版社,2005.
[45]宋百玲.柴油机BP神经网络调速快速控制原型研究[J].小型内燃机与摩托车,2009,38(2):50-53.
[46]上海交通大学应用数学系.概率论与数理统计初步[M].上海:上海交通大学出版社,1989.
[47]梁锋.高压共轨式电控柴油机故障自诊断研究[D].上海:上海交通大学,2004.
[48]Montgomery D C.Introduction to Statistical Quality Control[M].4th Edition.New York:John Wiley,2001.
[49]Simon Haykin.Neural networks:a comprehensive foundation[M].2~(nd)ed.USA:Prentice Hall,1998:443-483.
[50]沈铖武,王志乾,刘畅,等.BP神经网络在多位置捷联寻北系统中的应用[J].光学精密工程,2009,17(8):1890-1895.
[51]张奎.制动器结构和摩擦材料对制动性能的影响及设计方法探讨[J].重型汽车,2005,2:16-17.
[2]李长河.柴油机共轨喷射系统的发展及关键技术[J].内蒙古民族大学学报,2003,18(2):138-141.
[3]李绍安.国内外共轨系统的开发进展和所面临的问题[J].内燃机燃油喷射和控制,1999(4):5-8.
[4]梁峰,杨林,谭文春等.高压共轨式电控柴油机故障诊断策略仿真研究[J].内燃机学报,2004,22(6):532-537.
[5]徐权奎,祝轲卿,陈自强等.高压共轨柴油机喷油器电磁阀故障诊断系统设计[J].内燃机工程,2007,28(4):69-74.
[6]梁锋,肖文雍,谭文春等.高压共轨式电控柴油机电子油门故障自诊断策略研究[J].汽车技术,2003,7:18-22.
[7]蒋开正,毛俊.电控高压共轨柴油喷射系统故障诊断与维护[J].内燃机,2008,4(2):55-57.
[8]卢兴艳.基于图像处理的高压共轨喷油器故障诊断研究[D].大连:大连理工大学,2008.
[9]虞和济.故障诊断的基本原理[M].北京:冶金工业出版社,1989.
[10]鄂加强.智能故障诊断及其应用[M].湖南:湖南大学出版社,2006.
[11]黄泉水,江国和,肖建昆.基于AR模型和神经网络的柴油机故障诊断[J].噪声与振动控制,2008,3:60-63,91.
[12]FRANK P M.Analytical and qualitative model-based fault diagnosis—a survey and some new results[J].European Journal of Control,1996,2(1):6-28.
[13]Bernard F.On the properties of reduced order Kalman filters[J].IEEE Transactions on Automatic Control,1989,34(3):321-324.
[14]黄显林,卢鸿谦,王宇飞.组合导航非等间隔联合滤波[J].中国惯性技术学报,2002,10(3):1-7.
[15]Julier S J,Uhlmann J K.Unscented filtering and nonlinear estimation[J].Proceedings of the IEEE,2004,92(3):401-421.
[16]黄晓雷,牛申格.扩展卡尔曼滤波在无人机航迹控制系统中的研究应用[J].航空兵器,2008(2):11-13.
[17]王宏禹.随机数字信号处理[M].北京:科学出版社,1988.
[18]Gao Weibing.Variable Structure Control:A Survey[J].IEEE Transactions on Industrial Electronica,1993,40(1):2-22.
[19]Yang C D,Chen HY.Nonlinear Robust Guidance Law for Homing Missiles[J].Journal of Guidance,Control and Dynamics,1998,21(6):882-890.
[20]杨行峻,郑君里.人工神经网络[M].北京:高等教育出版社,1992.
[21]Simon H.Neural Networks A Comprehensive Foundation[M].北京:清华大学出版社,1998.
[22]周志华,曹存根.神经网络及其应用[M].北京:清华大学出版社,2004.
[23]Remelhart.D.E,et al.Learning internal representations by back-propagating errors[J].Nature,1986:533-536.
[24]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2000.
[25]焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社,1990.
[26]HAYKIN S著.神经网络原理[M].叶世伟,史忠植,译.北京:机械工业出版社,2003.
[27]KWONG C K,BAI H.A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment[J].Journal of Intelligent Manufacturing,2002,13(5):367-377.
[28]BAO J,ZHAO J Y,ZHOU H Y.Study on method of curve simulation based on BP network[J].Computer Engineering and Design,2005,26(7):840-841.
[29]虞和济,陈长征,张省,等.基于神经网络的智能诊断[M].北京:冶金工业出版社,2000.
[30]周文华.高压共轨电控喷油系统控制算法研究[J].内燃机工程,2002,23(4):30-33.
[31]刘兴堂.应用自适应控制[M].陕西:西北工业大学出版社,2003.
[32]Richard C.Dorf,Rebert H.Bi sshop著.现代控制系统[M].赵千川,冯妹,译.北京:清华大学出版社,2008.
[33]Kreisselmeier G.Adaptive control via Adaptive Observer and Asymptotic Feedback Matrix Synthesis[J].IEEE Transactions on Automatic Control,1980,25:717-722.
[34]Narendra K S,Valavani L S.Direct and Indirect Model Reference Adaptive Control[J].Automatic,1979,15(6):653-664.
[35]Lin Y,Narendra K S.A New Error Model for Adaptive System[J].IEEE Transactions on Automatic Control,1980,25(3):585-587.
[36]李慧光,武波,杨晨影.Delta算子控制及鲁棒控制理论基础一统——连续域、离散域的控制理论[M].北京:国防工业出版社,2005.
[37]张瑞金,吴捷.基于Delta算子的自适应信号处理[J].系统工程与电子技术,2001,5:4-7.
[38]FAN H,DE P.High speed adaptive signal processing using the Delta operator[J].Digital Signal Processing,A Review Journal,2001,11(1):3-34.
[39]AGARWAL R C,BURROWS C S.New recursive digital filter structures having low sensitivity and round-off noise[J].IEEE Transactions on Circuits and Systems,1975,22(2):921-927.
[40]GOODWIN G C,LEAL R L,MIDDLETON R H.Rapprochement between continuous and discrete model reference adaptive control[J].Automatic,1986,22(2):199-207.
[41]李慧光,张尚斌,王墨琦.基于前向差分Delta算子的LMS自适应滤波算法[J].数据采集与处理,2000,24(2):189-192.
[42]宋雪洁,张端金,王香丽.基于Delta算子的SVD—LMS算法[J].现代电子技术,2006,11:79-80,83.
[43]Stepaniak M J,et al.MMAE-Based Control Redistribution Applied to the VISTA F-16.IEEE Transactions on Aerospace and Electronic Systems.1998,34(4):1249-1260.
[44]吴振顺.自适应控制理论与应用[M].黑龙江:哈尔滨工业大学出版社,2005.
[45]宋百玲.柴油机BP神经网络调速快速控制原型研究[J].小型内燃机与摩托车,2009,38(2):50-53.
[46]上海交通大学应用数学系.概率论与数理统计初步[M].上海:上海交通大学出版社,1989.
[47]梁锋.高压共轨式电控柴油机故障自诊断研究[D].上海:上海交通大学,2004.
[48]Montgomery D C.Introduction to Statistical Quality Control[M].4th Edition.New York:John Wiley,2001.
[49]Simon Haykin.Neural networks:a comprehensive foundation[M].2~(nd)ed.USA:Prentice Hall,1998:443-483.
[50]沈铖武,王志乾,刘畅,等.BP神经网络在多位置捷联寻北系统中的应用[J].光学精密工程,2009,17(8):1890-1895.
[51]张奎.制动器结构和摩擦材料对制动性能的影响及设计方法探讨[J].重型汽车,2005,2:16-17.