An interpretable evolutionary broad learning system for damage identification in aircraft structures using Lamb waves

Abstract

Deep learning (DL) has gained significant attention in Lamb wave-based structural health monitoring (SHM). However, existing DL approaches for damage identification in aircraft structures require manually designed network architectures tailored to specific tasks, resulting in substantial computational overhead and hindering real-time monitoring applications. To overcome these limitations, this study proposes a novel damage identification method for aircraft structures based on Lamb waves and an interpretable evolutionary broad learning system (EBLS), which can automatically learn the complex nonlinear relationship between damage features in Lamb wave signals and structural health conditions. The proposed method incorporates a novel particle swarm optimization with square wave switching mechanism (SWSPSO) to systematically explore and optimize the complex hyperparameter space of the broad learning system (BLS). This intelligent optimization enables automated generation of task-specific BLS architectures for damage identification without manual intervention. The interpretability of EBLS is rigorously investigated through locally interpretable model-agnostic explanations (LIME), revealing physically meaningful correlations between critical feature contributions and fundamental Lamb wave propagation characteristics. Experimental validation employs a comprehensive Lamb wave dataset acquired through lead zirconate titanate (PZT) sensors mounted on aircraft structural components, encompassing diverse damage scenarios with varying locations and severity levels. Experimental results demonstrate that EBLS significantly outperforms traditional deep learning models, achieving over 0.95 accuracy in damage identification tasks while reducing computational efficiency by an order of magnitude and enhancing interpretability.