Research On Structural Health Monitoring Based On Active Lamb Wave Oriented To Aircraft Structure

Structure health monitoring (SHM) is very important to mechanical structures and so on, especially to the aerospace engineering structures. SHM is an effective way to get the condition of the structures in-situ and real-time. The technology can realize the diagnosis of structure's health condition by itself, providing the safety estimation, and predicting the residual life to insure the structures'safety and decrease the mending cost.Structure health monitoring technology based on active Lamb wave which is one way of SHM was researched, and function demonstration on engineering structure was done in the study. The main works can be generalized as following:1.The characteristic of piezoelectric component and the basic theory of Lamb wave were discussed. The piezoelectric formula which describes the piezoelectric effect and both phase and group velocity of Lamb wave were proposed. The method to excite Lamb wave was experimentally researched and the excited signal especially the choice of the signal's center frequency was analyzed.2.Active damage detection of composite structure was discussed. By calculating the arrival time difference of the disperse signal aroused by damage using active lamb wave and wavelet transform, damage position can be detected according to ellipse localization method. To overcome the dead area of detection by ellipse localization method, optimization collocation of piezoelectric sensors with star topology form was proposed with multi-path scanning, which widened the range of monitoring and improved the accuracy of damage identification. To some extent, the damage area also could be determined.3.The net scanning system for damage identification based on collocation of piezoelectric sensors with star topology. The study of hardware was mainly including the construction of signal output and input system, and the development of circuit board. The software included the development of user interface and the signal processing program. 4.In allusion to the rivet failure during the strength test of unmanned vehicle wing-box, taking BP neural network to identify the damage based on the characteristic of Lamb wave. The different levels of rivet failure were classified.