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Electronics
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Advances in Inspection and Sensing Technologies
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Advances in Inspection and Sensing Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 5484

Special Issue Editors

Dr. Fang Cheng

E-Mail Website
Guest Editor
Singapore Institute of Manufacturing Technology (SIMTech), Singapore 637662, Singapore
Interests: dimensional measurement; surface finish measurement; precision engineering; instrumentation; metrology; machine vision; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Tegoeh Tjahjowidodo

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Katholieke Universiteit Leuven, 3001 Leuven, Belgium
Interests: characterization, modelling and control of mechanical systems comprising material and geometrical nonlinearities; Tool Wear; Condition Monitoring; cutting force
Special Issues, Collections and Topics in MDPI journals
Dr. Andrew Alexander Malcolm

E-Mail Website
Guest Editor
Advanced Remanufacturing and Technology Centre (ARTC), A*STAR, Singapore 637143, Singapore
Interests: 3D image processing; image analysis; X-ray; computed tomography; reverse engineering; machine vision; industrial inspection; NDT

Special Issue Information

Dear Colleagues,

High-performance inspection and sensing technologies have become key enablers for advanced manufacturing, especially in the industries of semiconductor, consumer electronics, precision engineering, and fast-moving consumer goods (FMCG). As the frontier of data collection, these technologies are also the critical elements for Industry 4.0.

Inspection and sensing technologies have seen rapid development in the recent decade, driven by emerging concepts in the areas of electronics, optics, quantum physics, digitalization, and artificial intelligence.
As inspection and sensing applications involve multi-discipline topics, this Special Issue will welcome researchers from different areas to contribute high-quality submissions. The topics of interest will include, but not be limited to, the following:

  • Intelligent inspection
  • Sensing technologies
  • Sensor fusion
  • Image/data fusion
  • Machine vision
  • Machine learning and deep learning
  • Condition monitoring and predictive maintenance
  • Non-destructive testing (NDT)
  • Optics and photonics
  • In-line inspection and instrumentation

Dr. Fang Cheng
Prof. Tegoeh Tjahjowidodo
Dr. Andrew Alexander Malcolm
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. Electronics 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 2400 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)

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Research

25 pages, 6529 KiB  
Article
Rapid Non-Invasive Capacitive Assessment of Extra Virgin Olive Oil Authenticity
by Hari Krishna Salila Vijayalal Mohan, Pyei Phyo Aung, Chee Fong Ng, Zheng Zheng Wong and Andrew Alexander Malcolm
Electronics 2023, 12(2), 359; https://doi.org/10.3390/electronics12020359 - 10 Jan 2023
Cited by 2 | Viewed by 1806
Abstract
Economically motivated adulteration (EMA) and/or cross-contamination are the two major factors resulting in the substandard quality of premium edible oil like extra virgin olive oil (EVOO) produced in food and beverage (F&B) fast-moving consumer goods (FMCG) industries. Current quality assurance methods (e.g., spectroscopy [...] Read more.
Economically motivated adulteration (EMA) and/or cross-contamination are the two major factors resulting in the substandard quality of premium edible oil like extra virgin olive oil (EVOO) produced in food and beverage (F&B) fast-moving consumer goods (FMCG) industries. Current quality assurance methods (e.g., spectroscopy and chromatography) in FMCG involve intrusive sample extraction and ex situ analysis in a laboratory using expensive bulky instrumentation, which is neither integrable inline nor scalable to match the production throughput. Such techniques do not meet the industrial requirements of in situ testing, non-intrusive analysis, and high throughput inspection (100% product verification) leading to food loss and package waste from unwanted batch rejects. Herein, a low-cost electrical approach based on capacitance is proposed to show the proof of concept for screening EVOO-filled containers non-invasively for adulteration without any sample extraction by capturing the differences in the dielectric properties of mixed oils. The sensor system displayed a fast response (100 ms) and low detection limits for different adulterants (olive oil (32.8%), canola oil (19.4%), soy oil (10.3%) and castor oil (1.7%)), which is suitable for high-throughput (>60 sample/min) screening. Furthermore, a low-cost automated system prototype was realized to showcase the possibility of translating the proof of concept for possible scaling up and inline integration. Full article
(This article belongs to the Special Issue Advances in Inspection and Sensing Technologies)
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22 pages, 4071 KiB  
Article
ACPA-Net: Atrous Channel Pyramid Attention Network for Segmentation of Leakage in Rail Tunnel Linings
by Peng Geng, Ziye Tan, Jun Luo, Tongming Wang, Feng Li and Jianghui Bei
Electronics 2023, 12(2), 255; https://doi.org/10.3390/electronics12020255 - 4 Jan 2023
Cited by 1 | Viewed by 1511
Abstract
The automatic segmentation of leakage in rail tunnel linings is a useful and challenging task. Unlike other scenarios, the complex environment inside the tunnels makes it difficult to obtain accurate results for the segmentation of leakages. Some deep learning-based methods have been used [...] Read more.
The automatic segmentation of leakage in rail tunnel linings is a useful and challenging task. Unlike other scenarios, the complex environment inside the tunnels makes it difficult to obtain accurate results for the segmentation of leakages. Some deep learning-based methods have been used to automatically segment tunnel leakage, but these methods ignore the interdependencies between feature channels, which are very important for extracting robust leakage feature representations. In this work, we propose an atrous channel pyramid attention network (ACPA-Net) for rail tunnel lining leakage segmentation. In ACPA-Net, the proposed atrous channel pyramid attention (ACPA) module is added into a U-shaped segmentation network. The ACPA module can effectively strengthen the representation ability of ACPA-Net by explicitly modeling the dependencies between feature channels. In addition, a deep supervision strategy that helps ACPA-Net improve its discrimination ability has also been introduced into ACPA-Net. A rail tunnel leakage image dataset consisting of 1370 images with manual annotation maps is built to verify the effectiveness of ACPA-Net. The final experiment shows that ACPA-Net achieves state-of-the-art performance on the Crack500 dataset and our rail tunnel leakage image dataset, and our method has the least number of parameters of all the methods. Full article
(This article belongs to the Special Issue Advances in Inspection and Sensing Technologies)
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17 pages, 16288 KiB  
Article
Uniform Circular-Array-Based Borehole Pulsed Eddy-Current System for Asymmetry Defect Inspection in Downhole Casings
by Ling Yang, Bo Dang, Zhiping Ren, Changzan Liu, Jingxin Dang, Yang Zhao, Baixin An and Ruirong Dang
Electronics 2022, 11(13), 2030; https://doi.org/10.3390/electronics11132030 - 28 Jun 2022
Cited by 2 | Viewed by 1305
Abstract
The inspection of wellbore casings has been extensively investigated owing to the increasing concern for safety in oil and gas production. However, efficient techniques for inspecting asymmetry defects have not been achieved. In this study, we developed a uniform circular array (UCA) to [...] Read more.
The inspection of wellbore casings has been extensively investigated owing to the increasing concern for safety in oil and gas production. However, efficient techniques for inspecting asymmetry defects have not been achieved. In this study, we developed a uniform circular array (UCA) to address the problem of borehole pulsed eddy current (PEC) techniques for asymmetry defect inspection in downhole casings. Based on the borehole PEC system model, the UCA developed with multiple independent probes was designed to achieve asymmetry defect inspection, and the three-dimensional magnetic field data of borehole depths, circumferential azimuths, and sampling times could be obtained. Furthermore, a multichannel data acquisition circuit, which guarantees downhole operation at 150 °C, was developed for the synthesized UCA. Using azimuth dimension information from the synthesized UCA at a certain borehole depth, we obtained an inspection approach for the width and penetration depth of asymmetry defects in the circumferential and radial directions, respectively. Simulations and field experiments were conducted, and the results demonstrate the effectiveness of the proposed method in inspecting asymmetry defects. Full article
(This article belongs to the Special Issue Advances in Inspection and Sensing Technologies)
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