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
Industrial Sensor Enabled Smart Computing for Design, Control and Maintenance...
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Industrial Sensor Enabled Smart Computing for Design, Control and Maintenance in Real Time Applications

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 6504

Special Issue Editors

Dr. Sachin Kumar

E-Mail Website
Guest Editor
Department of Computer Science, South Ural State University (National Research University), Chelyabinsk 454080, Russia
Interests: IoT; machine learning; industrial sensor; intelligent transportation; smart city; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Prayag Tiwari

E-Mail Website
Guest Editor
School of Information Technology, Halmstad University, Halmstad, Sweden
Interests: natural language processing; quantum physics; deep learning; cognitive science; healthcare; IoT

Special Issue Information

Dear Colleagues,

Modern sensors are a vital component in industrial applications as they enable the reading and sensing of measurement properties that can predict failure, track levels, and enhance manufacturing efficiencies for monitoring, control, and predictive maintenance. Various types of wired and wireless sensors, including level, electric current, humidity, pressure, proximity, heat, and flow sensors, are among the most widely used in industrial applications. Moreover, several other types of sensors are used in the healthcare industry to monitor a patient’s condition. Other types of sensors are being used in smart vehicles to support traffic safety features. The sensors gather a lot of information that can be stored, managed, and analyzed for critical decision making and predictive maintenance in real-time applications.

This Special Issue will feature state-of-the-art research addressing the broad challenges involved in developing sensors for industrial, healthcare, and transportation applications and analyzing sensor data using big data methods. We are seeking original, unpublished research/review papers that are not currently under review by any other journal, magazine, or conference. Our goal is to build a community of authors and practitioners to discuss the latest ideas and research directions. The topics covered include (but are not limited to):

  • Sensors for the smart manufacturing environment;
  • Wearable sensors for smart healthcare;
  • Impact of sensors on manufacturing efficiency;
  • Deep learning for sensor data analysis;
  • Predictive maintenance using sensor data analysis;
  • Internet-of-Things-enabled decision making in industrial applications;
  • Artificial Intelligence;
  • Applications and case studies on modern and upcoming sensor technologies;
  • Sensor-enabled smart vehicles ;
  • Sensor-enabled intelligent transportation system.

Dr. Sachin Kumar
Dr. Prayag Tiwari
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

  • sensors for the smart manufacturing environment
  • wearable sensors for smart healthcare
  • impact of sensors on manufacturing efficiency
  • deep learning for sensor data analysis
  • predictive maintenance using sensor data analysis
  • Internet of Things enabled decision making in industrial applications
  • artificial intelligence
  • applications and case studies on modern and upcoming sensor technologies
  • sensor enabled smart vehicles
  • sensor enabled intelligent transportation system

Published Papers (4 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

25 pages, 4459 KiB  
Article
RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines
by Chathurangi Shyalika, Kaushik Roy, Renjith Prasad, Fadi El Kalach, Yuxin Zi, Priya Mittal, Vignesh Narayanan, Ramy Harik and Amit Sheth
Sensors 2024, 24(10), 3244; https://doi.org/10.3390/s24103244 - 20 May 2024
Viewed by 401
Abstract
Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry [...] Read more.
Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines. Full article
(This article belongs to the Special Issue Industrial Sensor Enabled Smart Computing for Design, Control and Maintenance in Real Time Applications)
►▼ Show Figures

Figure 1

20 pages, 4667 KiB  
Article
Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing
by Tsung-Hsien Liu, Jun-Zhe Chi, Bo-Lin Wu, Yee-Shao Chen, Chung-Hsun Huang and Yuan-Sun Chu
Sensors 2023, 23(1), 284; https://doi.org/10.3390/s23010284 - 27 Dec 2022
Viewed by 1453
Abstract
The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant [...] Read more.
The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN’s learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%. Full article
(This article belongs to the Special Issue Industrial Sensor Enabled Smart Computing for Design, Control and Maintenance in Real Time Applications)
►▼ Show Figures

Figure 1

12 pages, 3846 KiB  
Article
Sensitivity Optimization of Surface Acoustic Wave Yarn Tension Sensor Based on Elastic Beam Theory
by Yong Ding, Lili Gao and Wenke Lu
Sensors 2022, 22(23), 9368; https://doi.org/10.3390/s22239368 - 1 Dec 2022
Cited by 1 | Viewed by 1111
Abstract
The measurement of yarn tension has a direct impact on the product quality and production efficiency in the textile manufacturing process, and the surface acoustic wave (SAW) yarn tension sensor is a good option for detecting the yarn tension. For SAW yarn tension [...] Read more.
The measurement of yarn tension has a direct impact on the product quality and production efficiency in the textile manufacturing process, and the surface acoustic wave (SAW) yarn tension sensor is a good option for detecting the yarn tension. For SAW yarn tension sensors, sensitivity is an important indicator to assess their performance. In this paper, a new type of SAW yarn tension sensor based on a simply supported beam structure is studied to improve the sensitivity of the fixed beam SAW yarn tension sensor. The sensitivity analysis method based on elastic beam theory is proposed to illustrate the sensitivity optimization. According to the analysis results, the sensitivity of the SAW yarn tension sensor can be greatly improved by using a simply supported beam structure compared to the s fixed beam structure. Moreover, from the calibration experiment, the sensitivity of the simply supported beam SAW yarn tension sensor is 2.5 times higher than that of the fixed beam sensor. Full article
(This article belongs to the Special Issue Industrial Sensor Enabled Smart Computing for Design, Control and Maintenance in Real Time Applications)
►▼ Show Figures

Figure 1

19 pages, 6191 KiB  
Communication
SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System
by Mohammed Abdou and Hanan Ahmed Kamal
Sensors 2022, 22(23), 9108; https://doi.org/10.3390/s22239108 - 24 Nov 2022
Cited by 1 | Viewed by 2622
Abstract
Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity [...] Read more.
Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird’s eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively. Full article
(This article belongs to the Special Issue Industrial Sensor Enabled Smart Computing for Design, Control and Maintenance in Real Time 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 1-4
Back to TopTop