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Special Issue "Smart Sensors Application in Predictive Maintenance"

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 739

Special Issue Editors

Prof. Dr. Constantin Volosencu
E-Mail Website
Guest Editor
Department of Automation, 'Politehnica' University of Timisoara, 300006 Timisoara, Romania
Interests: control systems; applications of sensor networks; fuzzy control; applications of neural networks; control of electric drives; power ultrasound applications
Special Issues, Collections and Topics in MDPI journals
Dr. Boon-Chong Seet
E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
Interests: 5G wireless communications; antennas and radio frequency based sensors; smart textile and wearable technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Some research directions for smart sensors can be considered, as follows. Sensors will become real smart sensors, characterized by the following: intelligent measurement units that self-monitor, transmit status diagnoses to the operating system, and create a reliable network of measurement and calibration data. Sensors will be used for the maintenance and security of machines and devices. Predictive maintenance for machines and devices will become increasingly more efficient, easier, cheaper, and improve uptime. In the future, maintenance will rely on sensors instead of being carried out according to a needs-based timetable. Safety will also improve because unsafe situations will be easily predicted. Autonomous wireless-connected sensors will be possible. Sensors will be self-learning over the entire lifespan without maintenance, modifications, or calibration. The possibilities and areas of application for robot technology will increase significantly. Old and new technologies at the chip level are arising. Transmitters, receivers, and printed circuit boards are becoming increasingly smaller, which will lead to more possibilities in sensor fusion. Synthetic sensors will be developed. Sensors will increasingly provide a better understanding of human behavior. Moreover, components will take over the role of human senses. Data will become more reliable and be collected continuously. Data will be converted into useful information using intelligent software and algorithms. This will lead humans to set other requirements with respect to air quality, travel, automobile maintenance, lifestyle, insurance, energy consumption, etc. Fully automated management of livestock is possible. Precision agriculture will also be within reach. Farmer’s yields will improve so much that they will be better able to compete with high-quality yields and crop yields. Sensors will be increasingly used to research soil quality, climate, crops, diseases, plagues, and weeds. New control systems will equip autonomous vehicles with real vision. Cities will become more intelligent and ecosystem-friendly. Flood management, air quality, parking, safe playgrounds, monumental trees will remain, and soil conditions will improve. Sensors will improve the environment, improve energy management, and build green office buildings. Sensor technology will be integrated into every aspect of human lives.

Prof. Dr. Constantin Volosencu
Dr. Boon-Chong Seet
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 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.


  • smart sensors
  • machine and device maintenance
  • autonomous sensors
  • self learning techniques
  • robots
  • sensor fusion
  • human behavior
  • human senses
  • agriculture
  • ecosystems

Published Papers (1 paper)

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Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions
Sensors 2022, 22(10), 3947; - 23 May 2022
Viewed by 426
Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating [...] Read more.
Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry. Full article
(This article belongs to the Special Issue Smart Sensors Application in Predictive Maintenance)
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