利用振动声调制信号的小波熵识别多螺栓连接松动的 RFE 辅助变压器-SVM 框架

IF 2.1 4区 工程技术 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
Xiao-Xue Li, Dan Li, Wei-Xin Ren, Xiang-Tao Sun
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引用次数: 0

摘要

为了确保结构的安全性和完整性,我们开发了一种新颖的框架,利用振动声学调制(VAM)信号的小波熵来检测多螺栓连接的松动情况。小波熵被用作动态指数,以捕捉表明连接状态的复杂时频特征。以小波熵向量为输入,所提出的框架通过整合用于高维特征提取的变换器模型和用于基本特征选择的递归特征消除(RFE),以及用于分类的支持向量机(SVM)模型而与众不同。具体来说,具有创新位置编码功能的 Transformer 模型有助于提取对螺栓松动敏感的随时间变化的瞬态特征。RFE 流程在降低数据维度的同时,还能辨别诊断信息,从而实现更准确的分类。通过对一个四螺栓关节的实验,交叉验证的识别结果表明,所提出的框架在各种松动情况下都具有很高的准确性和鲁棒性。它优于传统的 SVM、长短期记忆网络(LSTM)、卷积神经网络(CNN)-SVM 模型(无 RFE 和有 RFE),以及 Transformer-SVM 模型(无 RFE),准确率分别提高了 15.72%、11.74%、9.47%、5.49% 和 5.06%。事实证明,所提出的框架能够更有效地从小波熵数据中学习损伤敏感特征,这标志着在高强度螺栓连接工程结构的健康监测方面取得了重大进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An RFE-aided Transformer-SVM framework for multi-bolt connection loosening identification using wavelet entropy of vibro-acoustic modulation signals
To ensure structural safety and integrity, a novel framework is developed for detecting the loosening of multi-bolt connections using wavelet entropy of vibro-acoustic modulation (VAM) signals. Wavelet entropy is employed as the dynamic index to capture the intricate time-frequency characteristics that are indicative of the connection status. Taking the wavelet entropy vectors as input, the proposed framework distinguishes itself by integrating a Transformer model for high-dimensional feature extraction with the recursive feature elimination (RFE) for essential feature selection, followed by a support vector machine (SVM) model for classification. Specifically, the Transformer model with innovative positional encoding capability helps to extract the time-dependent transient features that are sensitive to the bolt loosening. The RFE process reduces the data dimensionality while discerning the diagnostic information for more accurate classification. Through the experiment on a four-bolt joint, the identification results with cross-validation showed high accuracy and robustness of the proposed framework across various loosening cases. It outperformed the traditional SVM, long short-term memory network (LSTM), convolutional neural network (CNN)-SVM models without and with RFE, as well as the Transformer-SVM model without RFE, achieving an accuracy increase of 15.72%, 11.74%, 9.47%, 5.49%, and 5.06%, respectively. The proposed framework was demonstrated to be able to learn the damage-sensitive features more effectively from wavelet entropy data, marking a significant advancement in the health monitoring of engineering structures with high-strength bolt connections.
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来源期刊
Advances in Structural Engineering
Advances in Structural Engineering 工程技术-工程:土木
CiteScore
5.00
自引率
11.50%
发文量
230
审稿时长
2.3 months
期刊介绍: Advances in Structural Engineering was established in 1997 and has become one of the major peer-reviewed journals in the field of structural engineering. To better fulfil the mission of the journal, we have recently decided to launch two new features for the journal: (a) invited review papers providing an in-depth exposition of a topic of significant current interest; (b) short papers reporting truly new technologies in structural engineering.
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