Multifrequency Identification and Exploitation in Lamb Wave Inspection

Lamb wave inspection provides a promising method to assess the structural health status. However, Lamb wave modes exhibit different characteristics which vary with frequency significantly. The best excitation frequency usually cannot be determined in specific applications. This work proposes a multifrequency exploitation and identification method. Lamb waves of multiple frequencies are excitated simultaneously to utilize diverse attributes of Lamb waves in different frequency ranges. This paper firstly analyzes the detectability and sensitivity of Lamb wave. Then the multifrequency exploitation scheme and corresponding post-processing method are introduced. Relevant simulations by finite element method are conducted to verify its effectiveness. Experiments of single-frequency and multifrequency excitations are implemented. The investigations indicate that the proposed method can avoid the missing of defects compared with single-frequency excitation. In addition, a post-processing method is suggested and the results demonstrate that the multifrequency excitation also provides high accuracy in defect location

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version