Corrosion quantitative monitoring based on dual-modal electromechanical impedance and bidirectional multilevel fusion network

Abstract

Electromechanical impedance (EMI) technology has been widely used in structural health monitoring, yet its potential has been limited by conventional impedance measurement methods. This study proposes a novel axial and bending modal excitation method for EMI measurement, enabling simultaneous capture of dual-modal vibration responses. Based on these dual-modal characteristics, a novel bidirectional multilevel fusion network framework for EMI signal processing was proposed. The innovations of this study include (1) a logarithmic frequency dual attention module was designed, where first-order derivative attention mechanism captures instantaneous signal changes, second-order derivative attention mechanism captures curvature features, and fusion attention combines the advantages of both, improving the training accuracy of difficult-to-fit signals to 92.5%. (2) A bidirectional-attention multistage fusion network was proposed. This network adopts an innovative additive fusion strategy to effectively avoid gradient vanishing, fully utilizes the complementary features of axial and bending vibration modes, achieves deep information fusion and synergistic enhancement, enables the model to comprehensively grasp corrosion characteristics from different dimensions, and improves the training accuracy from 82.04% and 92.5% in single-modal scenarios to 100%. The method also demonstrated excellent noise resistance under various signal-to-noise ratio conditions, maintaining reliable performance in complex monitoring environments. These results confirm that the proposed dual-modal measurement method, combined with the fusion framework, provides an enhanced solution for EMI-based damage detection, offering improved sensitivity and reliability. This work establishes a new paradigm for EMI signal acquisition and processing in structural health monitoring applications.

Keywords

Electromechanical impedance dual-modal vibration multilevel fusion network damage detection structural health monitoring

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