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Sensors
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Advanced Sensing Systems for Structural Monitoring and Damage Detection
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Advanced Sensing Systems for Structural Monitoring and Damage Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1199

Special Issue Editors

Dr. Magda Ruiz

E-Mail Website
Guest Editor
Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: structural health monitoring; damage detection; damage sensitive
Dr. Luis Eduardo Mujica

E-Mail Website
Guest Editor
Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: vera popovic structural health monitoring; signal processing

Special Issue Information

Dear Colleagues,

Structural health monitoring uses advanced sensing and data analytics to continuously assess infrastructure condition, detect damage early, and enable timely repairs. New technologies, like fiber optic sensors and AI algorithms, analyze real-time sensor data to identify abnormalities indicative of flaws, providing 24/7 monitoring and actionable information on both local damage and global performance. In this sense, the main objective of this Special Issue is to provide a space to present these advances, which could revolutionize the monitoring of civil infrastructure health.

Dr. Magda Ruiz
Dr. Luis Eduardo Mujica
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural health monitoring (SHM)
  • assessing condition and performance
  • early detection of damage/deterioration
  • repairs and maintenance of structures
  • advanced sensing technologies
  • real-time distributed data
  • artificial intelligence
  • statistical learning
  • machine learning
  • data science
  • feature extraction
  • pattern recognition

Published Papers (2 papers)

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Research

20 pages, 7187 KiB  
Article
A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring
by Yilin Xie, Xiaolin Meng, Dinh Tung Nguyen, Zejun Xiang, George Ye and Liangliang Hu
Sensors 2024, 24(10), 3163; https://doi.org/10.3390/s24103163 - 16 May 2024
Viewed by 423
Abstract
This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an [...] Read more.
This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors’ experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)
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20 pages, 11148 KiB  
Article
A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis
by Xiao Yang, Fengrong Bi, Jiangang Cheng, Daijie Tang, Pengfei Shen and Xiaoyang Bi
Sensors 2024, 24(9), 2708; https://doi.org/10.3390/s24092708 - 24 Apr 2024
Viewed by 510
Abstract
Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and [...] Read more.
Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve “Dead ReLU” and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)
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