Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Distributed Sensing and Imaging Technologies for Health Monitoring Applications
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Distributed Sensing and Imaging Technologies for Health Monitoring Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 25 December 2024 | Viewed by 2571

Special Issue Editors

Prof. Dr. Ram Bilas Pachori

E-Mail Website
Guest Editor
Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
Interests: signal and image processing; biomedical signal processing; non-stationary signal processing; speech signal processing; brain–computer interfacing; machine learning; AI and IoT in healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. M. Sabarimalai Manikandan

E-Mail Website
Guest Editor
Department of Electrical Engineering, Indian Institute of Technology Palakkad, Palakkad, Kerala, India
Interests: signal and image processing; internet of things; machine learning/AI

Special Issue Information

Dear Colleagues,

Health monitoring and damage identification of structural systems are essential to extend the working life of structures subjected to aging degradation, allowing the increase in safety and reliability and the optimization of proactive maintenance operations. Recent advances in sensors and computing technologies has provided an unprecedented opportunity to complement traditional structural health monitoring (SHM) and non-destructive evaluation (NDE) approaches. In addition, signal and image analysis techniques play an important role in noise removal (denoising), feature extraction, dimensionality reduction, data compression and encryption, which are an integral part of many health monitoring systems that perform automatic event detection, classification, localization and prediction systems in various applications of structural health monitoring, vehicle health monitoring and machine health monitoring, environmental health monitoring, precision agriculture and power quality monitoring. Smart health monitoring systems generally include single or multimodal sensors, data acquisition, signal and image processors, and wireless radios that can enable the detection, processing, analysis, control, storage and transmission of different kinds of single-modal or multimodal signals and images that are integrated with wireless sensor networks (WSNs), internet of things (IoTs) and autonomous vehicles (AVs), which may be constrained with computational resources for the development of affordable edge computing-based monitoring solutions.

The main purpose of this Special Issue to include the recent advancements in the areas of distributed sensing and imaging technologies for health monitoring, the objectives of energy-efficient event detection, classification and prediction in edge computing environments. The research topics include, but are not limited to, the following:

  • Deep Learning-Based Structural Health Monitoring Systems;
  • Imaging Technologies for Structural Health Monitoring;
  • Distributed Sensing Technologies for Structural Health Monitoring;
  • Sensing Technologies for Environmental Health Monitoring;
  • Sensing Technologies for Crop Health Monitoring Networks;
  • Soil Vital Sensing Techniques for Nutrients Monitoring;
  • Imaging Technologies for Crop Health Monitoring;
  • Deep Learning-Based Crop Disease Classification Systems;
  • Sensing Technologies for Autonomous Vehicle (Ground Vehicle, Surface Vehicle, Underwater Vehicle, Aerial/Flying Vehicle) Health Monitoring;
  • Deep Learning-Based Machine Condition Monitoring;
  • Deep Learning-Based Edge Vehicle Health Monitoring for Autonomous Vehicles.

Prof. Dr. Ram Bilas Pachori
Dr. M. Sabarimalai Manikandan
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.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 7363 KiB  
Article
Deep Learning-Based Prognostics and Health Management Model for Pilot-Operated Cryogenic Safety Valves
by Minho Kim, Hansaem Seong and Dohyun Kim
Sensors 2024, 24(6), 1814; https://doi.org/10.3390/s24061814 - 12 Mar 2024
Viewed by 923
Abstract
This paper highlights the significance of safety and reliability in modern industries, particularly in sectors like petroleum and LNG, where safety valves play a critical role in ensuring system safety under extreme conditions. To enhance the reliability of these valves, this study aims [...] Read more.
This paper highlights the significance of safety and reliability in modern industries, particularly in sectors like petroleum and LNG, where safety valves play a critical role in ensuring system safety under extreme conditions. To enhance the reliability of these valves, this study aims to develop a deep learning-based prognostics and health management (PHM) model. Past empirical methods have limitations, driving the need for data-driven prediction models. The proposed model monitors safety valve performance, detects anomalies in real time, and prevents accidents caused by system failures. The research focuses on collecting sensor data, analyzing trends for lifespan prediction and normal operation, and integrating data for anomaly detection. This study compares related research and existing models, presents detailed results, and discusses future research directions. Ultimately, this research contributes to the safe operation and anomaly detection of pilot-operated cryogenic safety valves in industrial settings. Full article
(This article belongs to the Special Issue Distributed Sensing and Imaging Technologies for Health Monitoring Applications)
►▼ Show Figures

Figure 1

19 pages, 4095 KiB  
Article
Semantic Segmentation of Surface Cracks in Urban Comprehensive Pipe Galleries Based on Global Attention
by Yuan Zhou, Zhiyu Yang, Xiaofeng Bai, Chengwei Li, Shoubin Wang, Guili Peng, Guodong Li, Qinghua Wang and Huailei Chang
Sensors 2024, 24(3), 1005; https://doi.org/10.3390/s24031005 - 04 Feb 2024
Viewed by 500
Abstract
Cracks inside urban underground comprehensive pipe galleries are small and their characteristics are not obvious. Due to low lighting and large shadow areas, the differentiation between the cracks and background in an image is low. Most current semantic segmentation methods focus on overall [...] Read more.
Cracks inside urban underground comprehensive pipe galleries are small and their characteristics are not obvious. Due to low lighting and large shadow areas, the differentiation between the cracks and background in an image is low. Most current semantic segmentation methods focus on overall segmentation and have a large perceptual range. However, for urban underground comprehensive pipe gallery crack segmentation tasks, it is difficult to pay attention to the detailed features of local edges to obtain accurate segmentation results. A Global Attention Segmentation Network (GA-SegNet) is proposed in this paper. The GA-SegNet is designed to perform semantic segmentation by incorporating global attention mechanisms. In order to perform precise pixel classification in the image, a residual separable convolution attention model is employed in an encoder to extract features at multiple scales. A global attention upsample model (GAM) is utilized in a decoder to enhance the connection between shallow-level features and deep abstract features, which could increase the attention of the network towards small cracks. By employing a balanced loss function, the contribution of crack pixels is increased while reducing the focus on background pixels in the overall loss. This approach aims to improve the segmentation accuracy of cracks. The comparative experimental results with other classic models show that the GA SegNet model proposed in this study has better segmentation performance and multiple evaluation indicators, and has advantages in segmentation accuracy and efficiency. Full article
(This article belongs to the Special Issue Distributed Sensing and Imaging Technologies for Health Monitoring Applications)
►▼ Show Figures

Figure 1

21 pages, 25693 KiB  
Article
Detecting Multiple Damages in UHPFRC Beams through Modal Curvature Analysis
by Fahime Sokhangou, Luca Sorelli, Luc Chouinard, Pampa Dey and David Conciatori
Sensors 2024, 24(3), 971; https://doi.org/10.3390/s24030971 - 02 Feb 2024
Viewed by 692
Abstract
Curvature-based damage detection has been previously applied to identify damage in concrete structures, but little attention has been given to the capacity of this method to identify distributed damage in multiple damage zones. This study aims to apply for the first time an [...] Read more.
Curvature-based damage detection has been previously applied to identify damage in concrete structures, but little attention has been given to the capacity of this method to identify distributed damage in multiple damage zones. This study aims to apply for the first time an enhanced existing method based on modal curvature analysis combined with wavelet transform curvature (WTC) to identify zones and highlight the damage zones of a beam made of ultra-high-performance fiber-reinforced concrete (UHPFRC), a construction material that is emerging worldwide for its outstanding performance and durability. First, three beams with a 2 m span of UHPFRC material were cast, and damaged zones were created by sawing. A reference beam without cracks was also cast. The free vibration responses were measured by 12 accelerometers and calculated by operational modal analysis. Moreover, for the sake of comparison, a finite element model (FEM) was also applied to two identical beams to generate numerical acceleration without noise. Second, the modal curvature was calculated for different modes for both experimental and FEM-simulated acceleration after applying cubic spline interpolation. Finally, two damage identification methods were considered: (i) the damage index (DI), based on averaging the quadratic difference of the local curvature with respect to the reference beam, and (ii) the WTC method, applied to the quadratic difference of the local curvature with respect the reference beam. The results indicate that the developed coupled modal curvature WTC method can better identify the damaged zones of UHPFRC beams. Full article
(This article belongs to the Special Issue Distributed Sensing and Imaging Technologies for Health Monitoring Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop