运行汽车工作模态分析研究
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摘要
传统的汽车模态识别方法是在良好的实验室控制条件下，用力锤等激振器激振车体，以此获得准确的导纳函数，来识别汽车的模态参数。这种方法费时费力，成本很高，而且由于悬架系统的预应力和非线性等因素使汽车的实际工作情况与实验室的控制状况存在较大的差异，识别精度也存在一系列问题，因此有必要进行工作环境下的模态识别研究，即在激励信号未知情况下利用响应信号来识别模态参数。
针对传统模态辨识方法的缺陷，本文研究了工程结构在环境激励下工作模态的辨识问题。分别通过原始数据和相关函数数据构成Hankel矩阵，运用子空间方法进行系统矩阵的辨识。经采用悬臂梁有限元计算和实验分析对比验证，上述方法拥有极高的辨识精度。
本文论述了用子结构工作模态识别方法成功识别了一微型车的工作模态，并探讨了工作模态分析实际应用情况。结果显示子结构工作模态识别方法能在工作条件下识别汽车结构模态参数，说明用工作模态识别方法来获得汽车结构的动态特性是非常有效的一种方法。
Classically, the modal parameters of a automobile are derived from FRF measurements in well-controlled laboratory conditions using hammer of shaker excitation, this requires additional measurement costs and time. However, the modal vibro-acoustic behavior of an automobile on the road may differ significantly from the one during the laboratory test due to e.g. pre-stress and non-linear behavior of the suspension system in a classic modal analysis in laboratory more modes will be identified, some modes which are less important in the operational conditions. Hence, the need arises to identify a modal model of a automobile in driving condition. In this case, only response data are measurable while the actual loading condition is unknown.
Aiming at the defects of classical modal parameter estimation approach, this article studies the technique for modal parameter extraction from structures under operation conditions. The method for system matrix determination is presented by using a block Hankel matrix of raw data and correlation functions. Subsequently, the performance of the methods is critically evaluated for cantilever beam. The results of experiment show that this methods possess the same accuracy as the classical modal estimation method.
In this paper, the capabilities and limitations of the subspace identification techniques will be studied on response data on a microbus in driving conditions, The industrial applicability of using this kind of data for a modal analysis has been successfully investigated by doing some analysis on data measured in normal conditions. It has been shown how the modal parameters from in-operation output-only data can be extracted by using correlation-driven stochastic subspace techniques. In-operation modal analysis proves to be a valuable tool for identifying the actual dynamic characteristics of a structure of automobile in use.
针对传统模态辨识方法的缺陷，本文研究了工程结构在环境激励下工作模态的辨识问题。分别通过原始数据和相关函数数据构成Hankel矩阵，运用子空间方法进行系统矩阵的辨识。经采用悬臂梁有限元计算和实验分析对比验证，上述方法拥有极高的辨识精度。
本文论述了用子结构工作模态识别方法成功识别了一微型车的工作模态，并探讨了工作模态分析实际应用情况。结果显示子结构工作模态识别方法能在工作条件下识别汽车结构模态参数，说明用工作模态识别方法来获得汽车结构的动态特性是非常有效的一种方法。
Classically, the modal parameters of a automobile are derived from FRF measurements in well-controlled laboratory conditions using hammer of shaker excitation, this requires additional measurement costs and time. However, the modal vibro-acoustic behavior of an automobile on the road may differ significantly from the one during the laboratory test due to e.g. pre-stress and non-linear behavior of the suspension system in a classic modal analysis in laboratory more modes will be identified, some modes which are less important in the operational conditions. Hence, the need arises to identify a modal model of a automobile in driving condition. In this case, only response data are measurable while the actual loading condition is unknown.
Aiming at the defects of classical modal parameter estimation approach, this article studies the technique for modal parameter extraction from structures under operation conditions. The method for system matrix determination is presented by using a block Hankel matrix of raw data and correlation functions. Subsequently, the performance of the methods is critically evaluated for cantilever beam. The results of experiment show that this methods possess the same accuracy as the classical modal estimation method.
In this paper, the capabilities and limitations of the subspace identification techniques will be studied on response data on a microbus in driving conditions, The industrial applicability of using this kind of data for a modal analysis has been successfully investigated by doing some analysis on data measured in normal conditions. It has been shown how the modal parameters from in-operation output-only data can be extracted by using correlation-driven stochastic subspace techniques. In-operation modal analysis proves to be a valuable tool for identifying the actual dynamic characteristics of a structure of automobile in use.
引文
[1] 沃德．海伦 斯蒂芬．拉门兹 波尔．萨斯 模态分析理论与试验 北京理工大学出版社 2001．6
[2] 许本文 焦群英 机械振动与模态分析基础 机械工业出版社 1998．8
[3] 郑敏 中凡 陈怀海 鲍明 利用互相关函数进行环境激励下的模态分析 航空学报 2000 Vol．21 No．6 P．535-537
[4] 史东锋 郑敏 申凡 鲍明 工程结构工作模态的子空间辨识方法 振动工科学报 2000 Vol．13 No．3 P．406-412
[5] 秦仙蓉 张令弥 对模态参数识别的整体正交多项式算法的评述 强度与环境 2000 No．4 P．30-38
[6] 张阿舟等．实用振动工程——振动理论与分析．航空工业出版社
[7] 李建平．小波分析与信号处理．重庆：重庆出版社 1997
[8] 张令弥．振动测试与动态分析．航空工业出版社 1992
[9] 张思等．振动测试与分析技术．清华大学出版社 1992
[10] 方远祥等．振动模态分析技术．国防工业出版社 1993
[11] W．T．汤姆逊．振动理论极其应用．煤炭工业出版社 1980
[12] 沈宏竣等．双层客车车体的模态分析．振动与冲击．1992．(1-2)77
[13] 夏禾 陈英俊．多维 AR模型在桥梁模态脉冲分析法中的应用．振动与冲击．1993．(1)45
[14] 廖炳棋．海浪运动数字模拟ARMA法．振动与冲击．1993．(3)47
[15] 顾家柳 用模式识别理论指导振动诊断．振动与冲击．1991．(2)39
[16] 廖炳祺．ARMA模型参数确定方法的改进．振动与冲击．1991．(2)38
[17] 魏来生．结构有限元动态模型修正方法综述．振动与冲击．1998．(3)43
[18] 李舜酩．许庆余．李永平．行驶车辆振动信号的小波分析．汽车工程．1997．19(6)370～375
[19] 陈志奎等．工程信号中的小波采样与小波分析．重庆大学学报(自然科学版)1996．19(5)1～9
[20] 李克强等．汽车车内噪声因素的结构振动影响率分析法及应用．汽车工程．1996．18(1)14～19
[21] 王志杰 丁康．汽车变速器故障振动特征提取的试验研究．汽车工程．1994．16(4) 242～249．
[22] 王晚枝．车身结构振动与车内噪声藕合的研究．汽车工程．1993．(1)278～283
[23] 续秀忠等．基于环境激励的模态参数方法综述．振动与冲击．2002．(3)
[24] 靳晓雄 周宏 轿车后座噪声卢源识别研究．汽车工程．1997．19(3)148～12
[25] Jeroen Dewer, Benoit Dierckx MODAL PARAMETERS FROM DATA MEASURED ON A CAR IN OPERATING CONDITIONS LMS International Interleuvenlaan68 3001 Leuven Belgium
[26] H. Van der Auweraer, L.Hermans, B.Dierckx,J.Deweer IN-OPERATION MODAL ANALYSIS: THEORY AND APPLICATION LMS International Leuven Belgium
[27] H.Vam Der Auweraer, L.Hermans,B.Dierckx,J.Deweer USING IN-OPERATION MODAL ANALYSIS FOR STRUCTURAL DYNAMICS ASSESSMENT AND MONITORING LMS International Leuven Belgium
[28] L.Hermans,P..Mas, W. Leurs And N.Boucart ESTIMATION AND USE OF RESIDUAL MODES IN MODAL COUPLING CALCULATIONS: A CASE STUDY LMS International Interleuvenlaan 68 3001 Leuven Belgium
[29] Herman Ban Der Auweraer, Willem Leuts, Peter Mas,Luc Hermans MODAL PARAMETER ESTIMATON FROM INCONSISTENT DATA SETS LMS International Interleuvenlaan 68 3001 Leuven Belgium
[30] Katrien Wyckaert,Johan Van Keymeulen OPERRATIONAL ANALYSIS,TRANSFER PATH ANALYSIS, MODAL ANALYSIS:TOOLS TO UNDERSTAND ROAD NOISE KPLROBLEMS IN CARS. LMS International Interleuvenlaan 68 3001 Leuven Belgium
[31] L. Hermans,H. Van der Auweraer. MODAL TESTING AND ANALYSIS OF STEUCTURES UNDER OPERATIONAL CONDETIONS:INDUSTRIAL APPLICATIONS. LMS International B--3001, Leuven,Belgium
[32] Luc Hermans, Herman Van der Auweraer, Maher Abdelghani. MODAL TESTING AND ANALYSIS OF A CAR UNDER OPERATIONAL CONDETIONS. LMS International B--3001, Leuven,Belgium
[33] James G H, Garne T G. The natural excitation technique (NEXT) for modal param eter extraction from operating structures. Int. J. of Analytical & Experimental Modal Analysis, 1995; 10(4): 260—277
[34] James G H, Carne T G. Modal parameter extraction from large operational struct ure using ambient extraction. 14th SEM International Modal Analysis Confe rence, Dearborn, MI 1996
[35] Brown D, Allemang R, Zimmerman R, Mergeay M. Parameter Estimation Techniques For Modal Analysis. SAE-Paper 790221,1979,pp.19
[36] Pascual R, Golinval J C, Razeto M. A frequency domain coorelation technique Lembregts F. Parameter Estimation In Modal Analysis. Proceedings of ISMA 14. Leuven, Belgium, Seotember, 1989rnational Modal Analysis Conference, Orlando, Florida, 1997.
[37] N.Lms Cada-X User Manual:Link.Lms International,Leuven,Belgium
[38] N.Cada Modal/Analysis User ManuaI.Lms International,Leuven,Belgium
[39] Lembregts F. Parameter Estimation In Modal Analysis. Proceedings of ISMA 14. Leuven, Belgium, Seotember, 1989
[40] Aoki M.state space modeling of time series. Berlin: Springer-Verlag. 1990
[41] Verhaegen M, Dewilde P. Subspace modal identification part 1:the output-error state-space model identification class of algorithms. Int. J. Of control, 1992;56:1182.1210
[42] Juang J, Pappa R. An eigensystem realization algorithm for modal parameter identification and mokel reduction. J.of guidance,Control & Dynamics, 1985;8:620-627
[43] Peeters B,De Roeck G et al.Stodhastic Subspace Techniques Applied to Paremeter Identification of Civil Engineering Structures,Priceeding of New Advances in Modal Synthesis of Large Structures: Nonlinear, Damped and Nondeterminestic Cases, Lyon,France,Septenber 1995,151-162。
[2] 许本文 焦群英 机械振动与模态分析基础 机械工业出版社 1998．8
[3] 郑敏 中凡 陈怀海 鲍明 利用互相关函数进行环境激励下的模态分析 航空学报 2000 Vol．21 No．6 P．535-537
[4] 史东锋 郑敏 申凡 鲍明 工程结构工作模态的子空间辨识方法 振动工科学报 2000 Vol．13 No．3 P．406-412
[5] 秦仙蓉 张令弥 对模态参数识别的整体正交多项式算法的评述 强度与环境 2000 No．4 P．30-38
[6] 张阿舟等．实用振动工程——振动理论与分析．航空工业出版社
[7] 李建平．小波分析与信号处理．重庆：重庆出版社 1997
[8] 张令弥．振动测试与动态分析．航空工业出版社 1992
[9] 张思等．振动测试与分析技术．清华大学出版社 1992
[10] 方远祥等．振动模态分析技术．国防工业出版社 1993
[11] W．T．汤姆逊．振动理论极其应用．煤炭工业出版社 1980
[12] 沈宏竣等．双层客车车体的模态分析．振动与冲击．1992．(1-2)77
[13] 夏禾 陈英俊．多维 AR模型在桥梁模态脉冲分析法中的应用．振动与冲击．1993．(1)45
[14] 廖炳棋．海浪运动数字模拟ARMA法．振动与冲击．1993．(3)47
[15] 顾家柳 用模式识别理论指导振动诊断．振动与冲击．1991．(2)39
[16] 廖炳祺．ARMA模型参数确定方法的改进．振动与冲击．1991．(2)38
[17] 魏来生．结构有限元动态模型修正方法综述．振动与冲击．1998．(3)43
[18] 李舜酩．许庆余．李永平．行驶车辆振动信号的小波分析．汽车工程．1997．19(6)370～375
[19] 陈志奎等．工程信号中的小波采样与小波分析．重庆大学学报(自然科学版)1996．19(5)1～9
[20] 李克强等．汽车车内噪声因素的结构振动影响率分析法及应用．汽车工程．1996．18(1)14～19
[21] 王志杰 丁康．汽车变速器故障振动特征提取的试验研究．汽车工程．1994．16(4) 242～249．
[22] 王晚枝．车身结构振动与车内噪声藕合的研究．汽车工程．1993．(1)278～283
[23] 续秀忠等．基于环境激励的模态参数方法综述．振动与冲击．2002．(3)
[24] 靳晓雄 周宏 轿车后座噪声卢源识别研究．汽车工程．1997．19(3)148～12
[25] Jeroen Dewer, Benoit Dierckx MODAL PARAMETERS FROM DATA MEASURED ON A CAR IN OPERATING CONDITIONS LMS International Interleuvenlaan68 3001 Leuven Belgium
[26] H. Van der Auweraer, L.Hermans, B.Dierckx,J.Deweer IN-OPERATION MODAL ANALYSIS: THEORY AND APPLICATION LMS International Leuven Belgium
[27] H.Vam Der Auweraer, L.Hermans,B.Dierckx,J.Deweer USING IN-OPERATION MODAL ANALYSIS FOR STRUCTURAL DYNAMICS ASSESSMENT AND MONITORING LMS International Leuven Belgium
[28] L.Hermans,P..Mas, W. Leurs And N.Boucart ESTIMATION AND USE OF RESIDUAL MODES IN MODAL COUPLING CALCULATIONS: A CASE STUDY LMS International Interleuvenlaan 68 3001 Leuven Belgium
[29] Herman Ban Der Auweraer, Willem Leuts, Peter Mas,Luc Hermans MODAL PARAMETER ESTIMATON FROM INCONSISTENT DATA SETS LMS International Interleuvenlaan 68 3001 Leuven Belgium
[30] Katrien Wyckaert,Johan Van Keymeulen OPERRATIONAL ANALYSIS,TRANSFER PATH ANALYSIS, MODAL ANALYSIS:TOOLS TO UNDERSTAND ROAD NOISE KPLROBLEMS IN CARS. LMS International Interleuvenlaan 68 3001 Leuven Belgium
[31] L. Hermans,H. Van der Auweraer. MODAL TESTING AND ANALYSIS OF STEUCTURES UNDER OPERATIONAL CONDETIONS:INDUSTRIAL APPLICATIONS. LMS International B--3001, Leuven,Belgium
[32] Luc Hermans, Herman Van der Auweraer, Maher Abdelghani. MODAL TESTING AND ANALYSIS OF A CAR UNDER OPERATIONAL CONDETIONS. LMS International B--3001, Leuven,Belgium
[33] James G H, Garne T G. The natural excitation technique (NEXT) for modal param eter extraction from operating structures. Int. J. of Analytical & Experimental Modal Analysis, 1995; 10(4): 260—277
[34] James G H, Carne T G. Modal parameter extraction from large operational struct ure using ambient extraction. 14th SEM International Modal Analysis Confe rence, Dearborn, MI 1996
[35] Brown D, Allemang R, Zimmerman R, Mergeay M. Parameter Estimation Techniques For Modal Analysis. SAE-Paper 790221,1979,pp.19
[36] Pascual R, Golinval J C, Razeto M. A frequency domain coorelation technique Lembregts F. Parameter Estimation In Modal Analysis. Proceedings of ISMA 14. Leuven, Belgium, Seotember, 1989rnational Modal Analysis Conference, Orlando, Florida, 1997.
[37] N.Lms Cada-X User Manual:Link.Lms International,Leuven,Belgium
[38] N.Cada Modal/Analysis User ManuaI.Lms International,Leuven,Belgium
[39] Lembregts F. Parameter Estimation In Modal Analysis. Proceedings of ISMA 14. Leuven, Belgium, Seotember, 1989
[40] Aoki M.state space modeling of time series. Berlin: Springer-Verlag. 1990
[41] Verhaegen M, Dewilde P. Subspace modal identification part 1:the output-error state-space model identification class of algorithms. Int. J. Of control, 1992;56:1182.1210
[42] Juang J, Pappa R. An eigensystem realization algorithm for modal parameter identification and mokel reduction. J.of guidance,Control & Dynamics, 1985;8:620-627
[43] Peeters B,De Roeck G et al.Stodhastic Subspace Techniques Applied to Paremeter Identification of Civil Engineering Structures,Priceeding of New Advances in Modal Synthesis of Large Structures: Nonlinear, Damped and Nondeterminestic Cases, Lyon,France,Septenber 1995,151-162。