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Sensors in Intelligent Industrial Applications
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Sensors in Intelligent Industrial Applications

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

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 19539

Special Issue Editors

Dr. Alberto Tellaeche

E-Mail Website
Guest Editor
Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain
Interests: machine vision; image processing; robotics; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Hugo Landaluce

E-Mail Website
Guest Editor
Faculty of Engineering, University of Deusto, 48007 Bilbao, Spain
Interests: RFID; communication protocols and standards; wireless sensor networks; Internet of Things; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past century, the manufacturing industry has undergone a number of paradigm shifts. The next natural evolutionary step is to provide value by creating industrial assets with human-like intelligence. This will only be possible by further integrating sensors technology into the manufacturing processes in which industrial equipment is monitored and controlled for analysis.

This special issue aims to collect cutting-edge research works that are being carried out in a fast growing field of research, such as artificial intelligence applied to industry and production processes. You are invited to submit original research contributions in all related areas, In particular, submitted papers should clearly show novel contributions and innovative applications covering- but not limited to any of the following topics:

the use of sensors for human machine and robot interaction, 
the use of artificial intelligence and sensors for advanced control in robotic and industrial applications, 
IIoT applications in industry, 
the use of sensors and communications in distributed industrial applications,
Machine vision and pattern recognition techniques,
Cybersecurity in industrial sensor networks.

Prof. Dr. Alberto Tellaeche
Dr. Hugo Landaluce
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

  • Artificial intelligence in industry
  • distributed sensor applications
  • advanced sensor control
  • alternative robotic applications
  • computer vision
  • IIoT

Published Papers (7 papers)

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Research

21 pages, 5534 KiB  
Article
Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes
by Zhenqiang Wei, Shaohua Dong and Xuchu Wang
Sensors 2023, 23(9), 4546; https://0-doi-org.brum.beds.ac.uk/10.3390/s23094546 - 7 May 2023
Cited by 1 | Viewed by 1819
Abstract
Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in [...] Read more.
Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in seriously mutual occlusions captured by moving cameras, the accuracy and speed of petrochemical equipment tracking would be limited because of the false and missed tracking of equipment with extreme sizes and severe occlusion, due to image quality, equipment scale, light, and other factors. In this paper, a new multiple petrochemical equipment tracking method is proposed by combining an improved Yolov7 network with attention mechanism and small target perceive layer and a hybrid matching that incorporates deep feature and traditional texture and location feature. The model incorporates the advantages of channel and spatial attention module into the improved Yolov7 detector and Siamese neural network for similarity matching. The proposed model is validated on the self-built petrochemical equipment video data set and the experimental results show it achieves a competitive performance in comparison with the related state-of-the-art tracking algorithms. Full article
(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)
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15 pages, 3748 KiB  
Article
Automatic Ergonomic Risk Assessment Using a Variational Deep Network Architecture
by Theocharis Chatzis, Dimitrios Konstantinidis and Kosmas Dimitropoulos
Sensors 2022, 22(16), 6051; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166051 - 12 Aug 2022
Cited by 8 | Viewed by 2107
Abstract
Ergonomic risk assessment is vital for identifying work-related human postures that can be detrimental to the health of a worker. Traditionally, ergonomic risks are reported by human experts through time-consuming and error-prone procedures; however, automatic algorithmic methods have recently started to emerge. To [...] Read more.
Ergonomic risk assessment is vital for identifying work-related human postures that can be detrimental to the health of a worker. Traditionally, ergonomic risks are reported by human experts through time-consuming and error-prone procedures; however, automatic algorithmic methods have recently started to emerge. To further facilitate the automatic ergonomic risk assessment, this paper proposes a novel variational deep learning architecture to estimate the ergonomic risk of any work-related task by utilizing the Rapid Entire Body Assessment (REBA) framework. The proposed method relies on the processing of RGB images and the extraction of 3D skeletal information that is then fed to a novel deep network for accurate and robust estimation of REBA scores for both individual body parts and the entire body. Through a variational approach, the proposed method processes the skeletal information to construct a descriptive skeletal latent space that can accurately model human postures. Moreover, the proposed method distills knowledge from ground truth ergonomic risk scores and leverages it to further enhance the discrimination ability of the skeletal latent space, leading to improved accuracy. Experiments on two well-known datasets (i.e., University of Washington Indoor Object Manipulation (UW-IOM) and Technische Universität München (TUM) Kitchen) validate the ability of the proposed method to achieve accurate results, overcoming current state-of-the-art methods. Full article
(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)
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17 pages, 6610 KiB  
Article
Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer
by Jianwei Yang, Chang Liu, Qitong Xu and Jinyi Tai
Sensors 2022, 22(7), 2641; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072641 - 30 Mar 2022
Cited by 9 | Viewed by 2303
Abstract
The rotate vector (RV) reducer has a complex structure and highly coupled internal components. Acoustic emission (AE) signal, which is more sensitive to a weak fault, is selected for fault diagnosis of the RV reducer. The high sampling frequency and big data are [...] Read more.
The rotate vector (RV) reducer has a complex structure and highly coupled internal components. Acoustic emission (AE) signal, which is more sensitive to a weak fault, is selected for fault diagnosis of the RV reducer. The high sampling frequency and big data are the challenges for AE signal store and analysis. This study combines compressed sensing (CS) and convolutional neural networks. As a result, data redundancy is significantly reduced while retaining most of the information, and the analysis efficiency is improved. Firstly, the time-domain AE signal was projected into the compression domain to obtain the compression signal; then, the wavelet packet decomposition in the compressed domain was performed to obtain the information of each frequency band. Next, the frequency band information was sent into the input layer of the multi-channel convolutional layer, and the energy pooling layer mines the energy characteristics of each frequency band. Finally, the softmax classifier was used to classify and predict different fault types of RV reducers. The self-fabricated RV reducer experimental platform was used to verify the proposed method. The experimental results show that the proposed method can effectively extract the fault features in the AE signal of the RV reducer, improve the efficiency of signal processing and analysis, and achieve the accurate classification of RV reducer faults. Full article
(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)
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14 pages, 6361 KiB  
Article
Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network
by Fátima A. Saiz, Iñigo Barandiaran, Ander Arbelaiz and Manuel Graña
Sensors 2022, 22(3), 882; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030882 - 24 Jan 2022
Cited by 12 | Viewed by 3558
Abstract
This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it [...] Read more.
This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribution is the compact representation of the surface information achieved by combining photometric stereo images into a RGB image that is fed to a convolutional segmentation network trained for surface defect detection. We demonstrate the advantage of this compact surface imaging representation over the use of each photometric imaging source of information in isolation. An empirical analysis of the performance of the segmentation network on imaging samples of materials with diverse surface reflectance properties is carried out, achieving Dice performance index values above 0.83 in all cases. The results support the potential of photometric stereo in conjunction with our semantic segmentation network. Full article
(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)
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14 pages, 3236 KiB  
Communication
Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices
by Alberto Tellaeche Iglesias, Ignacio Fidalgo Astorquia, Juan Ignacio Vázquez Gómez and Surajit Saikia
Sensors 2021, 21(24), 8202; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248202 - 8 Dec 2021
Cited by 2 | Viewed by 3445
Abstract
The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already [...] Read more.
The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art. Full article
(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)
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18 pages, 5764 KiB  
Article
Semi-Supervised Training for Positioning of Welding Seams
by Wenbin Zhang and Jochen Lang
Sensors 2021, 21(21), 7309; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217309 - 3 Nov 2021
Cited by 7 | Viewed by 2148
Abstract
Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep [...] Read more.
Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement. Full article
(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)
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20 pages, 4995 KiB  
Article
On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures
by Alberto Tellaeche Iglesias, Miguel Ángel Campos Anaya, Gonzalo Pajares Martinsanz and Iker Pastor-López
Sensors 2021, 21(10), 3339; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103339 - 11 May 2021
Cited by 10 | Viewed by 2797
Abstract
Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are trained using only [...] Read more.
Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are trained using only defect free textured images for each type of analyzed texture, labeling the samples for the SVMs in an automatic way. This work is based on two image processing streams using image sensors: (1) the CA first processes the incoming image from the input to the output, producing a reconstructed image, from which a measurement of correct or defective image is obtained; (2) the second process uses the latent layer information as input to the SVM to produce a measurement of classification. Both measurements are effectively combined, making an additional research contribution. The results obtained achieve a percentage of success of 92% on average, outperforming results of previous works. Full article
(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)
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