Transmitter beamforming and weighted image fusion–based multiple signal classification algorithm for corrosion monitoring

Abstract

With the increase in aging aircrafts, corrosion monitoring has attracted much attention in the structural health monitoring area. Multiple signal classification has been gradually applied to structural health monitoring area as a new promising method because of its ability of directional scanning and the potential to monitor multiple signal sources. However, applying multiple signal classification algorithm to monitor real damage still faces some challenges. First, the scattered Lamb waves obtained using a single actuator is relatively weak, making the signal-to-noise ratio of the scattered signals low and resulting in the low precision of multiple signal classification–based monitoring. Second, linear sensor array–based structural health monitoring methods have the problem of blind area at the angles close to 0° and 180°. To meet these challenges and target at providing monitoring ability of both the position and severity of the damage, a novel transmitter beamforming and weighted image fusion–based multiple signal classification algorithm is proposed using a dual array that consists of two linear sensor arrays to enhance the amplitude of scattered Lamb waves from corrosion, improve its signal-to-noise ratio and eliminate the blind area. The corrosion severity can be evaluated by analyzing the largest eigenvalue of signal covariance matrix developed using the multiple signal classification algorithm. The proposed transmitter beamforming and weighted image fusion–based multiple signal classification algorithm is verified on aluminum plates with real corrosion damages at five stages. Experimental results show that the proposed method can realize corrosion monitoring with a good precision even at the blind monitoring area.