A systematic methodology is applied for performing parametric identification and health monitoring in the suspension substructures of complex vehicle models. The equations of motion of these models are derived by applying the method of finite elements. As a consequence, they involve a quite large number of degrees of freedom. In addition, they include strongly nonlinear terms. In particular, the main nonlinearities arise due to the function of the suspension dampers and springs. Moreover, the action of the bushings connecting the suspension subsystems to the vehicle body are also characterized by a strongly nonlinear behavior. Since the resulting number of degrees of freedom is large, an appropriate coordinate condensation technique is first applied. This reduces drastically the dimension of the original system and allows the application of numerical methods, which are effective for dynamical systems with relatively small dimension. More specifically, this feature is exploited here by applying a statistical system identification methodology in order to perform parametric identification and fault detection studies in the suspension subsystems of the vehicle models examined. In the second part of this study, the methodology developed is applied and yields numerical results related to parametric identification and fault detection in the suspensions of the vehicle systems examined, which exhibit strongly nonlinear behavior, even in the presence of considerable measurement noise and model errors.

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