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.

Since numerous sensors are needed to create a sensor array for the structural health monitoring of large-scale structures, the equipment quantity and cost considerably increase. This study proposes a sparse, triangle-shaped sensor array to identify, orient, and assess the degree of structural damage in composite constructions in order to overcome this shortcoming. The damage-scattered Lamb waves are recorded by the sparse sensor array with a variety of features that are then extracted and fed into the support vector machine (SVM) classification method. The location and severity of the damage in composite constructions can be determined by training the SVM model. The principal component analysis technique is used to compress the wave feature vectors while maintaining the majority of the damage information because the high dimension of the wave feature vectors required a significant amount of calculation during the training phase. Proof-of-concept tests show that the trained model, by utilizing the many properties of Lamb wave signals, can orient and define the degree of damage with excellent accuracy. Multiple Lamb wave properties can be used to make up for the triangle sensor array’s loss of damage information. In conjunction with the SVM, the triangle-shaped sensor array that was proposed in this study can efficiently make it easier to identify and characterize damage to large-scale structures while using fewer sensors.