Deep Learning-Based Structural Health Monitoring Using Vibration Signals Under Sensor Fault Conditions

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2025-01-01 Epub Date: 2025-12-04 DOI:10.1016/j.ifacol.2025.11.601

Lite Zhang , Yiwen Lu , Peng Tao , Zhenhua Wei

引用次数: 0

Abstract

Structural health monitoring (SHM) is critical for detecting degradation in engineering structures, but traditional methods face challenges with sensor faults and computational costs. This paper proposes a deep learning-based approach using single-sensor vibration data and Transformer networks to achieve robust damage detection despite sensor failures. By integrating cosine similarity, Bayesian optimization, and a modified loss function, the method balances accuracy and efficiency, enabling cost-effective global SHM while handling noisy or faulty sensor signals.

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传感器故障条件下基于振动信号的深度学习结构健康监测

结构健康监测（SHM）对于检测工程结构的退化至关重要，但传统的方法面临传感器故障和计算成本的挑战。本文提出了一种基于深度学习的方法，利用单传感器振动数据和变压器网络来实现传感器故障时的鲁棒损伤检测。通过整合余弦相似度、贝叶斯优化和改进的损失函数，该方法平衡了精度和效率，在处理噪声或故障传感器信号的同时实现了经济高效的全局SHM。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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