Modal Analysis Fast Calculation Method Research And Application On Heavy Truck

Modal analysis is a important subject in which the system’s dynamic parameters in themodal coordinate are estimated based on kinetic equation and input-output datas obtained frommodal experiments. In order to reduce the cost of experimentation and save computing time, thispaper proposed a set of methods and systems to realize modal analysis’ fast calculation methodsfor large-scale problems and complex structures. Model order reduction and parameteroptimization combined methods is proposed to achieve modal parameters’ fast calculation methodand used in heavy-duty truck’s cab and chassis respectively. Advanced software and hardwaretechnology are used to build complete fast modal analysis system and new direction is propose inthis paper. The main tasks are as follows.(1) A model order reduction and parameter optimization combined method is proposed torealize fast modal analysis in system’s software. In accordance with experimental modal analysissystem model, the paper achieve the system characteristic matrix reduction in least squarescomplex frequency domain experimental modal analysis useing indirect model order reductionmethod. With the parameterization of the characteristic matrix’s component which determine theparameter identification efficiency, the optimal design model is established and the parameters areglobally optimized and reduced using improved genetic algorithm. In the study of the fast timedomain operational modal analysis method, combined with minimum information criterionbalanced truncation method is used in stochastic subspace identification method driven by thecovariance to reduce system characteristic matrix order and keep system’s characteristic. Andgenetic algorithm optimization is also used to realize optimization and reduction of characteristicmatrix. At last, through comparison of computational efficiency and specific example application,modal analysis’ fast calculation methods is proved to be effective.(2) A FPGA technology and real-time system combined method is proposed to achieve fast,synchronized and accurate dynamic data acquisition in system’s hardware. Through computationalefficiency and stability comparation using improved FFT algorithm between software andhardware in realtime and unrealtime system, the hardware and system structure are proved to beeffective to realize fast modal analysis and establish solid foundation to online modal analysis.(3) Different approaches are proposed to do modal analysis to the heavy-duty truck’sdifferent parts according to the adaptive analysis method. Compare to whole vehicle, the cab’sbody-in-white was light and semi-independent, and also be not well excited in round environmentexcitation. So it is appropriate for experimenttal modal analysis. Comparative study betweenLeast-Squares Complex Exponential (LSCE) methd and Least Squares Complex Frequency (LSCF) method were done, and the result between each method and finite element modal analysis areproved to be similar and effective. The amendments based on the specific suggestions forimprovement was proved to be successful by practical application in markets. The concept of theratio between the first order modal frequency and the quality is put forward. And the originalcognition of over-improve the first order modal frequency through improving local quality iscorrected according to the lightweight trendency. Simultaneously, the fast modal analysis of theimproved cab body-in-white is done and the computational efficiency has a substantial increasingin conditions of ensuring the accuracy based on indirect model order reduction and parameteroptimazation combined method.(4) Operational modal analysis method on running vehicles using environmental excitationin heavy truck road testing is proposed for the mass is too large and inconvenience to be artificiallyimposed external excitation. It was proved that the probabilistic characteristics of time seriescomposed of acceleration samples under uneven stone road environment excitation were similar tothe Wiener distribution through time series analysis. Combined the power spectrum analysis result,time series properties was regards as meeting the requirements of stochastic space modal analysis.Balanced truncation method and genetic optimization algorithms was combined to used in theframe’s stochastic subspace modal identification method driven by the covariance to reduces thecomputation. Minimum Information Standards was used to determine the key parameters’ value topromote optimization algorithms’ efficiency. It met the requirements of the online modal analysisand guarantee the accuracy of modal parameter identification. Compared with the finite elementmodal analysis result of the frame under different operating conditions, it believed that the fastmodal analysis results were reliable.