A novel passive-to-active fusion method using neural network for structural damage localization under workload

Abstract

Advanced aircraft structures are susceptible to hazardous factors such as external impact while in operation. It is crucial to establish aircraft health-monitoring technology that enables online safety status evaluation of composite structures. However, the problem of low accuracy in structural damage localization under working load persists. This study proposes a progressive research methodology that employs the innovative idea of feature-level fusion. The methodology involves active guided wave mechanism analysis, guided wave feature extraction, adaptive compensation, and precise damage localization. An improved active damage localization method oriented by passive real-time strain sensing is proposed. Verification and validation experiments fully verify the feasibility, applicability, and accuracy of the method, achieving damage localization under working load. In essence, through passive to active mapping network at its core, this study has to some extent overcome the bottleneck problem of aircraft damage localization that is unreliable under working load.