TL;DR
This study proposes a data-driven SHM framework that integrates piezoelectric electromechanical impedance sensing with a hybrid one-dimensional convolutional neural network and self-attention model for robust joint condition assessment and outperformed three benchmark deep learning models.
SpectralTCN is proposed, a temporal convolutional network augmented with a Wavelet Gating Module (WGM) that performs learnable, data-dependent multi-resolution filtering within each convolutional block, suitable for near-real-time, online damage detection in continuous bridge monitoring.
In order to solve the problems of limited vibration signal acquisition, incomplete single-modal features, and sound signals being vulnerable to noise interference in gearbox fault diagnosis, a Multi-Channel Convolutional-Transformer Cross- Attention Diagnosis Model is proposed. Its core innovations are as follows: comb...
A framework that combines vibration-based and image-based damage assessment with explainable artificial intelligence and a data-driven digital twin to support a continuously updated data-driven digital twin for structural health monitoring is presented.
Abstract. High-performance mechanical component structural health monitoring (SHM) is a vital issue in contemporary engineering, especially in the aerospace, automotive, and industrial turbomachinery sectors where component failure may be disastrous. This article introduces a new AI-aided SHM framework with multimodal...
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