Detection of structural damage using the exponential sine sweep method
نویسندگان
چکیده
Structural damages can result in nonlinear dynamical responses. Thus, estimating the nonlinearities generated by damages potentially allows detecting them. In this paper, an original approach called the ES2D (Exponential Sine Sweep Damage Detection) is proposed for nonlinear damage detection. This approach is based on a damage index that reflects the ratio of the energy contained in the nonlinear part of the output versus the energy contained in its linear part. For this, we suppose that the system under study can be modeled as a cascade of Hammerstein models, made of branches in parallel composed of an elevation to the th power followed by a linear filter called the th order kernel. The Exponential Sine Sweep Method (ESSM) is then used to identify the linear and nonlinear parts of the model. Exponential sine sweeps are a class of sine sweeps that allow estimating a system’s first kernels in a wide frequency band from only one measurement. The ES2D method is illustrated experimentally on two actual composite plates with surface-mounted PZT-elements: one healthy and one damaged (impact). A given propagation path between a sensor and an actuator in the system is here under investigation. Using the ESSM, the first kernels modeling this propagation path are estimated for both the damaged and undamaged states. On the basis of these estimated first Kernels, the damage index is built. Its detecting efficiency and its insensitivity to environmental noise are then assessed.
منابع مشابه
Nonlinear structural damage detection based on cascade of Hammerstein models
Structural damages can result in nonlinear dynamical signatures that can significantly enhance their detection. An original nonlinear damage detection approach is proposed that is based on a cascade of Hammerstein models modelisation of the structure. This model is estimated by means of the Exponential Sine Sweep Method from only one measurement. On the basis of this estimated model, the linear...
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