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Conv-Transformer based few-shot learning for highly accurate multi-task structural health monitoring via piezoelectric impedance 压电阻抗高精度多任务结构健康监测中基于逆变变压器的少镜头学习
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-09 DOI: 10.1016/j.ymssp.2026.113967
Hanqiao Sun, Jingfeng Lu, Jiawen Xu, Ruqiang Yan
Impedance signals for structural health monitoring are often sparse and difficult to acquire in damaged conditions. Increasing the damage categories would significantly reduce accuracy. In this study, we propose a Conv-Transformer model that is capable of multi-task structural health monitoring, addressing the complexities of small sample datasets while handling multiple fault detection tasks, including mass loss and bolt loosening. The model enhances feature extraction by combining convolutional layers and multi-head attention within the Transformer encoder, focusing on the relative location of the peaks and the local feature of each peak in the impedance signals. These advantages enable highly accurate multi-task SHM with small samples of impedance signals. The proposed model is first trained on a large amount of data in mixed conditions and then fine-tuned with small sample data for an eight-class fault classification task. Experimental results show that the model demonstrates strong learning ability and cross-condition transferability, achieving an accuracy of 92.12% for multi-task damage identification, a 4.49% improvement over a conventional Transformer baseline. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses.
用于结构健康监测的阻抗信号通常是稀疏的,并且在受损条件下难以获取。增加伤害种类会显著降低精度。在本研究中,我们提出了一个能够进行多任务结构健康监测的convo - transformer模型,该模型在处理多个故障检测任务(包括质量损失和螺栓松动)的同时,解决了小样本数据集的复杂性。该模型通过结合卷积层和Transformer编码器内的多头关注来增强特征提取,重点关注阻抗信号中峰值的相对位置和每个峰值的局部特征。这些优点使高精度的多任务SHM具有小样本的阻抗信号。首先对混合条件下的大量数据进行训练,然后对小样本数据进行微调,完成8类故障分类任务。实验结果表明,该模型具有较强的学习能力和跨条件可移植性,对多任务损伤识别的准确率达到92.12%,比传统的Transformer基线提高4.49%。该方法可应用于建筑物、桥梁和桁架的健康状况识别。
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引用次数: 0
Cutting force modeling and machinability analysis for ultrasonic vibration-assisted thread milling of SiCp/Al composites 超声振动辅助螺纹铣削SiCp/Al复合材料切削力建模及可加工性分析
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113973
Ziyang Zhang, Daohui Xiang, Chaosheng Song, Shuaikun Yang, Yanqin Li, Peicheng Peng, Bo Li, Guofu Gao, Yanyan Yan, Jinglin Tong
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引用次数: 0
Composite control technology for rapid position alignment of inertial reference unit under high-frequency resonant constraints 高频谐振约束下惯性参考单元快速定位的复合控制技术
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113974
Tuo Weixiao, Wang Tianyu, Li Xingfei
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引用次数: 0
A novel adaptive multistep estimation algorithm for generating initial excitations of multi-input multi-output time waveform replication of non-stationary signals 一种用于非平稳信号多输入多输出时间波形复制产生初始激励的自适应多步估计算法
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113981
Yucheng Zhang, Siyu Ren, Huaihai Chen, Xudong He, Ronghui Zheng
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引用次数: 0
Quantification and propagation of uncertainties in electroacoustic absorbers due to temperature effects using Bayesian inference 基于贝叶斯推理的电声吸收器中温度效应不确定性的量化和传播
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113970
Leonardo Ferreira, Rafael Teloli, Emanuele De Bono, Morvan Ouisse
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引用次数: 0
Interval model calibration with response-consistent supervised learning network 响应一致监督学习网络的区间模型标定
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113942
Yanlin Zhao, Qi Yun, Sifeng Bi, Xing Wang, Michael Beer
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引用次数: 0
Dynamic modeling and fault feature analysis of ball-cage scraping in flywheel bearings 飞轮轴承球笼刮擦动力学建模及故障特征分析
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113963
Jianwen Wang, Tian He, Hong Wang, Xu Li
{"title":"Dynamic modeling and fault feature analysis of ball-cage scraping in flywheel bearings","authors":"Jianwen Wang, Tian He, Hong Wang, Xu Li","doi":"10.1016/j.ymssp.2026.113963","DOIUrl":"https://doi.org/10.1016/j.ymssp.2026.113963","url":null,"abstract":"","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"1 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A physics-augmented deep learning framework for structural dynamic load identification with FRF-guided state expansion 基于frf引导状态展开的结构动载荷识别物理增强深度学习框架
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113965
Xinhao An, Jilin Hou, Łukasz Jankowski, Qingxia Zhang
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引用次数: 0
Noise reduction of multi-rotor by adaptive phase angle control based on acoustic feedback 基于声反馈的多转子自适应相位角控制降噪
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113935
Hanru Liu, Lin Zhang, Hongjun Huang, Lei Zhu
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引用次数: 0
A multi-strategy enhanced algorithm for active impulsive noise control 主动脉冲噪声控制的多策略增强算法
IF 8.4 1区 工程技术 Q1 ENGINEERING, MECHANICAL Pub Date : 2026-02-06 DOI: 10.1016/j.ymssp.2026.113946
Jianfeng Luo, Kean Chen, Lei Yang, Jiyang Zhang, Fenghua Tian, Lei Wang, Junhuan Qiao
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引用次数: 0
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Mechanical Systems and Signal Processing
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