{"title":"基于有限元法和变压器的压电陶瓷监测系统材料衰减提取","authors":"Wenxuan Cao , Junjie Li","doi":"10.1016/j.ymssp.2025.113047","DOIUrl":null,"url":null,"abstract":"<div><div>Piezoelectric (PZT) monitoring systems have been demonstrated to be both sensitive and efficient for structural health monitoring. However, the attenuation information collected by the PZT monitoring system often contains components that are unrelated to material properties, referred to as material-independent attenuation. Examples of this include wave diffusion attention, force-electric conversion loss, and so on. These material-independent attenuations can obscure valuable information within the signal and hinder the discrimination of structural damage. This study proposed a novel approach for extracting the attenuation components that are dependent on material internal properties, termed material-dependent attenuation. The operating mode of the PZT monitoring system was initially analyzed, leading to the derivation of the total attenuation equation. Subsequently, a surrogate model for predicting material-independent attenuation was introduced, which integrates the convolutional neural network (CNN) with the Transformer. The CNN was structured as a multi-residual channel module to improve feature resolution, while the Transformer served to encode and filter the positional attributes of these features. The training data for this model was generated through numerical simulations. Finally, based on the total attenuation equation, the material-independent attenuation was eliminated from the total attenuation to obtain the material-dependent attenuation. Numerical simulation tests demonstrated that the proposed approach can accurately extract the material-dependent attenuation from the PZT monitoring system. Additionally, a field experiment was also designed to apply the proposed approach to invert the concrete dynamic permeability coefficient. The inversion results indicated that the proposed approach can accurately extract the material-dependent attenuation with considerable practicality and generalization.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"236 ","pages":"Article 113047"},"PeriodicalIF":8.9000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Extracting material-dependent attenuation from the PZT monitoring system based on FEM and transformer\",\"authors\":\"Wenxuan Cao , Junjie Li\",\"doi\":\"10.1016/j.ymssp.2025.113047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Piezoelectric (PZT) monitoring systems have been demonstrated to be both sensitive and efficient for structural health monitoring. However, the attenuation information collected by the PZT monitoring system often contains components that are unrelated to material properties, referred to as material-independent attenuation. Examples of this include wave diffusion attention, force-electric conversion loss, and so on. These material-independent attenuations can obscure valuable information within the signal and hinder the discrimination of structural damage. This study proposed a novel approach for extracting the attenuation components that are dependent on material internal properties, termed material-dependent attenuation. The operating mode of the PZT monitoring system was initially analyzed, leading to the derivation of the total attenuation equation. Subsequently, a surrogate model for predicting material-independent attenuation was introduced, which integrates the convolutional neural network (CNN) with the Transformer. The CNN was structured as a multi-residual channel module to improve feature resolution, while the Transformer served to encode and filter the positional attributes of these features. The training data for this model was generated through numerical simulations. Finally, based on the total attenuation equation, the material-independent attenuation was eliminated from the total attenuation to obtain the material-dependent attenuation. Numerical simulation tests demonstrated that the proposed approach can accurately extract the material-dependent attenuation from the PZT monitoring system. Additionally, a field experiment was also designed to apply the proposed approach to invert the concrete dynamic permeability coefficient. The inversion results indicated that the proposed approach can accurately extract the material-dependent attenuation with considerable practicality and generalization.</div></div>\",\"PeriodicalId\":51124,\"journal\":{\"name\":\"Mechanical Systems and Signal Processing\",\"volume\":\"236 \",\"pages\":\"Article 113047\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechanical Systems and Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0888327025007484\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanical Systems and Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888327025007484","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Extracting material-dependent attenuation from the PZT monitoring system based on FEM and transformer
Piezoelectric (PZT) monitoring systems have been demonstrated to be both sensitive and efficient for structural health monitoring. However, the attenuation information collected by the PZT monitoring system often contains components that are unrelated to material properties, referred to as material-independent attenuation. Examples of this include wave diffusion attention, force-electric conversion loss, and so on. These material-independent attenuations can obscure valuable information within the signal and hinder the discrimination of structural damage. This study proposed a novel approach for extracting the attenuation components that are dependent on material internal properties, termed material-dependent attenuation. The operating mode of the PZT monitoring system was initially analyzed, leading to the derivation of the total attenuation equation. Subsequently, a surrogate model for predicting material-independent attenuation was introduced, which integrates the convolutional neural network (CNN) with the Transformer. The CNN was structured as a multi-residual channel module to improve feature resolution, while the Transformer served to encode and filter the positional attributes of these features. The training data for this model was generated through numerical simulations. Finally, based on the total attenuation equation, the material-independent attenuation was eliminated from the total attenuation to obtain the material-dependent attenuation. Numerical simulation tests demonstrated that the proposed approach can accurately extract the material-dependent attenuation from the PZT monitoring system. Additionally, a field experiment was also designed to apply the proposed approach to invert the concrete dynamic permeability coefficient. The inversion results indicated that the proposed approach can accurately extract the material-dependent attenuation with considerable practicality and generalization.
期刊介绍:
Journal Name: Mechanical Systems and Signal Processing (MSSP)
Interdisciplinary Focus:
Mechanical, Aerospace, and Civil Engineering
Purpose:Reporting scientific advancements of the highest quality
Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems