Impact detection and location for a plate structure using least squares support vector machines

Abstract

Impact force magnitude detection and site location for clamped plates have direct relevance to the maintenance of aircraft and spacecraft structures. This article presents the impact identification of an aluminum plate structure based on least squares support vector machines. The structure is equipped with an array of surface-bounded piezoelectric sensors, which receive strain response signals excited by hammer impacts on different sites with varying magnitudes. To identify the magnitude and location of the impact, two features are extracted from each sensor signal by Hilbert transform, and intelligent models are established using least squares support vector machines. The regression models are then validated through experimental studies, which reveal that the least squares support vector machine–based approach achieves more reliable identification result and better detection accuracy than conventional artificial neural networks. In addition, an empirical index is presented to compare the system efficiency in terms of detection accuracy as well as the numbers of sensors and data sets. Results show that the proposed least squares support vector machine–based impact identification system affords a higher efficiency than the existing ones because it accomplishes a moderate detection accuracy along with greatly reduced hardware cost and workload.