Domain-separated capsule network for damage detection in aluminum plates under varying vibration conditions

Abstract

The 2024 aluminum alloy, known for its high strength and resistance to fatigue, is widely used in critical parts of aircraft such as wings and fuselages. Techniques that use ultrasonic guided waves for structural health monitoring are commonly applied to detect damage in metal plates. However, changes in environmental vibrations can alter the signals collected, greatly affecting the accuracy of damage identification in aluminum alloy plates. To tackle this challenge, a domain-separated capsule network (DS-CapsNet) has been developed to reduce the impact of environmental vibrations on the accuracy of damage detection. DS-CapsNet integrates a Capsule Network with an attention mechanism to extract and reconstruct damage-related features while minimizing vibration-induced interference. Additionally, a dynamic adversarial factor is introduced to optimize feature alignment between different domains, enhancing the robustness of the model. Moreover, a multi-head self-attention mechanism improves classification performance by effectively capturing complex damage features. Experimental results demonstrate that the proposed DS-CapsNet consistently outperforms a broad range of baseline models, including traditional classifiers, deep learning networks, and domain adaptation approaches, confirming its robustness under varying vibration conditions.