Guided waves-based structural health monitoring (SHM) methods have potential for practical applications, since they are sensitive to small damages and are able to realize large area monitoring. Among these methods, the Reconstruction Algorithm for Probabilistic Inspection (RAPID), using a Piezoelectric transducer (PZT) sensor array, is one of the most widely used imaging algorithms to perform active damage monitoring and localization. However, since the sensing paths are distributed inside the sensor array with the non-uniform density, the RAPID algorithm can only localize damage when it is occurring inside of the array. If the damage occurs outside of the array or both inside and outside of the array, that is, multi-type damage, the performance of RAPID algorithm would not be satisfactory. In this paper, a scattering coefficient-based RAPID algorithm with damage indexes separation and imaging fusion is proposed. The amplitude of damage scattered signal at the corresponding time of fight is adopted as the weight in the probability distribution function, and damage indexes are then classified into two categories in the RAPID algorithm for the inside and outside damage localization respectively. Finally, an experiment on the complex composite plate, with the center large hole and surrounding bolt holes, is carried out to verify this proposed method. Experimental results show that this method can realize multi-type damage localization with errors less than 40 mm.

Guided wave array-based structural health monitoring (SHM) is a promising solution for diagnosing damage in metal-connected structures. In this field, the reconstruction algorithm for probabilistic inspection (RAPID) is one of the most widely used algorithms for performing damage localization. In this paper, a density clustering RAPID based on an array-compensated damage index is proposed. A new probability distribution function was constructed based on a new damage index, which is adaptive to different elements in the sensor array to compensate for performance variation. Then, the imaging matrix of the RAPID algorithm was density-clustered to obtain the location and degree of damage. Finally, the method was verified by experiments on a stiffened aluminum plate. The experimental results demonstrate that the method achieves damage localization and enables quantitative damage diagnosis.

Guided waves based structural health monitoring methods are potential for practical applications, since they are sensitive to small damage and could realize large area monitoring. However, guided waves attenuate seriously on some structures, such as carbon fiber composite structures, honeycomb skin structures, thermal protection structures of silicone polymer composites, and so on. Specially, the low signal-to-noise ratio of guided waves, resulting from high attenuation, limits the accuracy of guided waves based methods. In addition, these high attenuation structures are usually anisotropic, which make the propagation velocity related damage imaging methods not accurate. To solve this problem, a novel confocal scanning based multiple signal classification (MUSIC) damage imaging method with array steering vectors correction is proposed in this paper. Confocal scanning is carried out to make the excitation guided waves focused on special positions and MUSIC algorithm is used to synthetic the received guided waves, scattered from damage, to further improve the damage imaging accuracy. At last, an experiment on the carbon fiber composite plate, considered as the high attenuation structure, is carried out to verify this proposed method. Experimental results show that this method can recognize damage imaging within 2° and 18 mm deviations in angle and distance, respectively.