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A new method is introduced here to identify modal parameters by fitting curves with multiple degrees of freedom to experimental data in frequency domain. Unknown parameters in our method are natural frequencies and modal damping ratios, and, from these parameters natural modes and residual terms are determined. In comparing our method with the conventional Van Loon's method, fewer unknown parameters are dealt with, and thus computing time is shorter with better convergence of solution. Simulation results obtained show that our method is useful especially in refering to many data simultaneously.

This paper concerns the Direct System Identification Method (hereafter referred to as DSIM) which allows accurate and quick determination of two groups of properties which exercise dominant effects on low frequency vibration of a vehicle body. The first group is the rigid body properties of an engine. The second group is the properties of each engine mount. Under the assumption that the engine/mount system is a rigid body, this paper makes theoretical discussion for using the DSIM to induce the parameters of an engine/mount system, and makes improvements for better correlation with experiments. Also mentioned is a comparison of this study with the experimental results and verification of consistency on those parameters obtained from DSIM to predict the accurate vehicle characteristics, along with the role this method will play in upgrading the technology of prediction analysis.

In this paper, a new method which directly identifies characteristic matrices (the mass, damping and stiffness matrices) of the mechanical structure using measured forces input and responses data is proposed. This algorithm is based upon the Maximum Likelihood Estimation, so that the accuracy of identified matrices is stable to experimental errors (random errors). After a theoretical formulation is performed, two examples are provided to illustrate and validate this algorithm. One is analytical example which identifies analytically generated data with random noises, and the other experimentarly identified engine/mount system of automobiles.

Two methods have been developed for real time identification and classification of the roughness pattern of road surfaces using the neural network. These methods are directly available both for semi-active and active vibration controls of cars. Accelerations of the rear wheel axis under the suspension are used as the input data for real time identification. The neural network which has acquired the informations of the seven typical roughness patterns is used for real time classification of actual road surfaces during driving. Validity and usefulness of these methods are verified by simulation.

This paper presents some considerations and an application to automotive's subframe concerning system identification method by characteristic matrices using modal parameters to obtain rigid-body properties of mechanical structure. The pendulum method was adopted as the conventional way to identify the rigid-body properties in automotive's engine components such as flywheel. In the case of the big, complex shaped and heavy structural component such as frame and vehicle body, this method is not adequate for the difficulty of building the huge fixture and making sure of the accuracy of identification. So, in this paper, the method identifying characteristic matrices using the modal parameters is applied to automotive's subframe.