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A method for an accurate estimation of natural frequencies using swept-sine acoustic excitation

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Abstract Early detection of cracks in engineering structures by vibration-based methods requires monitoring systems that can evaluate natural frequencies with high accuracy. This paper introduces a method for estimating the… Click to show full abstract

Abstract Early detection of cracks in engineering structures by vibration-based methods requires monitoring systems that can evaluate natural frequencies with high accuracy. This paper introduces a method for estimating the natural frequencies using features extracted from the vibrational response of structures excited to cross through the resonance. To this aim, an open-loop acoustic system able to ensure bidirectional excitation, in swept-sine or short-time mode, with controllable parameters was developed. Analyzing the structural response at the crossing area through the resonance, two particularities on which the method is based have been revealed. The first refers to the symmetry of the frequency shift at peak amplitude from the natural frequency, if positive and negative sweep rates are applied. Therefore, the natural frequency can be found as the average of the frequency values at the resonance. Also, the necessary time to achieve the peak amplitude after passing through the natural frequency is similar, irrespective to the sign of the sweep rate. This accomplishment allows finding the natural frequency from a graphical representation. Frequency estimation made for a test structure by employing the two procedures lead to convergent results. Comparing these results to those achieved by impulsive excitation, a better precision was remarked at the swept-sine excitation. With regard the simplicity, rapidity and stability of the method, the swept-sine excitation has been proved as the most effective.

Keywords: natural frequency; frequencies using; swept sine; frequency; excitation; natural frequencies

Journal Title: Mechanical Systems and Signal Processing
Year Published: 2019

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