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Smart Sensor and Digital Twin Technologies for Industrial Process Control...
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Smart Sensor and Digital Twin Technologies for Industrial Process Control and Monitoring

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

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 17623

Special Issue Editors

Dr. David F. Nettleton

E-Mail Website
Guest Editor
1. IRIS Technology Solutions, 08940 Cornellà, Spain
2. Department of Information and Communications Technology (DTIC), Universitat Pompeu Fabra, 08018 Barcelona, Spain
Interests: industrial data analysis and modeling; machine learning; artificial intelligence and online social network analysis
Dr. Houcine Hassan

E-Mail Website
Guest Editor
School of Design Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: real-time systems support; embedded systems; hardware/software co-design; ubiquitous computing; AI agent based systems; autonomous systems; sensor networks; robotic architectures; behaviour and emotional systems; application and scheduling integration; QoS

Special Issue Information

Dear Colleagues,

Recently, we have seen a growing interest in the deployment of smart/soft sensors and digital twins for industrial process control, monitoring and decision support, potentiated by new sensor technologies for data capture and more easily deployable artificial intelligence and advanced data processing techniques. These offer new opportunities for applications of interest to the process industries, such as predictive maintenance, real-time quality control and process optimization to reduce energy consumption, increase throughput, etc. Furthermore, the process industries are adapting to new requirements of the circular economy and green legislation, which can be facilitated by embedded decision support systems. Finally, advanced sensor systems such as NIR, hyperspectral imaging and thermal imaging open new possibilities for in-line quality monitoring for the pharmaceutical industry, food industry, bioplastics and plastic waste recycling, among others.

This Special Issue therefore aims to compile original applied research and review articles on recent advances, technologies, solutions, applications and new challenges in the field of smart sensors and digital twins for industrial process control.

Potential topics include, but are not limited to:

  • Soft/smart sensor for industrial process monitoring and control.
  • Digital twins for industrial process simulation, optimization and real-time decision support.
  • Computer vision sensors/cameras using machine learning/AI for in-line quality control.
  • HIS—hyper-spectral imaging for the identification of materials, defects and quality control.
  • NRI—near infra-red imaging (spectral data) for the identification of materials, defects and quality control.
  • Industrial sensors for end-point detection.
  • Sensor technology for collaborative robot control.
  • Sensors for automated robot picking.
  • Sensor applications for circular and green economy applications and optimization, waste recycling and bioplastic manufacturing.

This topic will fit with the scope of the Sensors journal as it will showcase the key role of the latest generation of sensors in data capture for advanced data processing, as well as some of the latest sensor hardware technologies for in-line monitoring and the recent advances in the development of “soft/smart sensors” to facilitate ‘edge-computing’ (thus, reducing data transmission and processing at a remote computer). All of this will have special relevance for applications to industrial process industries, such as real-time control and monitoring and quality control, among others.

Dr. David F. Nettleton
Dr. Houcine Hassan
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

  • smart sensors
  • digital twin
  • industrial process
  • artificial intelligence
  • machine learning
  • control and monitoring

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Published Papers (7 papers)

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Research

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26 pages, 75608 KiB  
Article
Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines
by Vinai George Biju, Anna-Maria Schmitt and Bastian Engelmann
Sensors 2024, 24(2), 330; https://doi.org/10.3390/s24020330 - 5 Jan 2024
Cited by 4 | Viewed by 2261
Abstract
The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and [...] Read more.
The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA). Full article
(This article belongs to the Special Issue Smart Sensor and Digital Twin Technologies for Industrial Process Control and Monitoring)
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23 pages, 6428 KiB  
Article
Smart Sensor Control and Monitoring of an Automated Cell Expansion Process
by David F. Nettleton, Núria Marí-Buyé, Helena Marti-Soler, Joseph R. Egan, Simon Hort, David Horna, Miquel Costa, Elia Vallejo Benítez-Cano, Stephen Goldrick, Qasim A. Rafiq, Niels König, Robert H. Schmitt and Aldo R. Reyes
Sensors 2023, 23(24), 9676; https://doi.org/10.3390/s23249676 - 7 Dec 2023
Viewed by 2085
Abstract
Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from [...] Read more.
Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react in real-time to key control parameter variations, adapt to different patient profiles, and optimize the process. The aim of the current work is to develop and calibrate smart sensors for their deployment in a real bioreactor platform, with adaptive control and monitoring for diverse patient/donor cell profiles. A set of contrasting smart sensors has been implemented and tested on automated cell expansion batch runs, which incorporate advanced data-driven machine learning and statistical techniques to detect variations and disturbances of the key system features. Furthermore, a ‘consensus’ approach is applied to the six smart sensor alerts as a confidence factor which helps the human operator identify significant events that require attention. Initial results show that the smart sensors can effectively model and track the data generated by the Aglaris FACER bioreactor, anticipate events within a 30 min time window, and mitigate perturbations in order to optimize the key performance indicators of cell quantity and quality. In quantitative terms for event detection, the consensus for sensors across batch runs demonstrated good stability: the AI-based smart sensors (Fuzzy and Weighted Aggregation) gave 88% and 86% consensus, respectively, whereas the statistically based (Stability Detector and Bollinger) gave 25% and 42% consensus, respectively, the average consensus for all six being 65%. The different results reflect the different theoretical approaches. Finally, the consensus of batch runs across sensors gave even higher stability, ranging from 57% to 98% with an average consensus of 80%. Full article
(This article belongs to the Special Issue Smart Sensor and Digital Twin Technologies for Industrial Process Control and Monitoring)
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19 pages, 3550 KiB  
Article
A Human Digital-Twin-Based Framework Driving Human Centricity towards Industry 5.0
by Gianfranco E. Modoni and Marco Sacco
Sensors 2023, 23(13), 6054; https://doi.org/10.3390/s23136054 - 30 Jun 2023
Cited by 16 | Viewed by 2870
Abstract
This work presents a digital-twin-based framework focused on orchestrating human-centered processes toward Industry 5.0. By including workers and their digital replicas in the loop of the digital twin, the proposed framework extends the traditional model of the factory’s digital twin, which instead does [...] Read more.
This work presents a digital-twin-based framework focused on orchestrating human-centered processes toward Industry 5.0. By including workers and their digital replicas in the loop of the digital twin, the proposed framework extends the traditional model of the factory’s digital twin, which instead does not adequately consider the human component. The overall goal of the authors is to provide a reference architecture to manufacturing companies for a digital-twin-based platform that promotes harmonization and orchestration between humans and (physical and virtual) machines through the monitoring, simulation, and optimization of their interactions. In addition, the platform enhances the interactions of the stakeholders with the digital twin, considering that the latter cannot always be fully autonomous, and it can require human intervention. The paper also presents an implemented scenario adhering to the proposed framework’s specifications, which is also validated with a real case study set in a factory plant that produces wooden furniture, thus demonstrating the validity of the overall proposed approach. Full article
(This article belongs to the Special Issue Smart Sensor and Digital Twin Technologies for Industrial Process Control and Monitoring)
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26 pages, 17352 KiB  
Article
Towards Recognition of Human Actions in Collaborative Tasks with Robots: Extending Action Recognition with Tool Recognition Methods
by Lukas Büsch, Julian Koch, Daniel Schoepflin, Michelle Schulze and Thorsten Schüppstuhl
Sensors 2023, 23(12), 5718; https://doi.org/10.3390/s23125718 - 19 Jun 2023
Cited by 6 | Viewed by 1952
Abstract
This paper presents a novel method for online tool recognition in manual assembly processes. The goal was to develop and implement a method that can be integrated with existing Human Action Recognition (HAR) methods in collaborative tasks. We examined the state-of-the-art for progress [...] Read more.
This paper presents a novel method for online tool recognition in manual assembly processes. The goal was to develop and implement a method that can be integrated with existing Human Action Recognition (HAR) methods in collaborative tasks. We examined the state-of-the-art for progress detection in manual assembly via HAR-based methods, as well as visual tool-recognition approaches. A novel online tool-recognition pipeline for handheld tools is introduced, utilizing a two-stage approach. First, a Region Of Interest (ROI) was extracted by determining the wrist position using skeletal data. Afterward, this ROI was cropped, and the tool located within this ROI was classified. This pipeline enabled several algorithms for object recognition and demonstrated the generalizability of our approach. An extensive training dataset for tool-recognition purposes is presented, which was evaluated with two image-classification approaches. An offline pipeline evaluation was performed with twelve tool classes. Additionally, various online tests were conducted covering different aspects of this vision application, such as two assembly scenarios, unknown instances of known classes, as well as challenging backgrounds. The introduced pipeline was competitive with other approaches regarding prediction accuracy, robustness, diversity, extendability/flexibility, and online capability. Full article
(This article belongs to the Special Issue Smart Sensor and Digital Twin Technologies for Industrial Process Control and Monitoring)
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14 pages, 3177 KiB  
Article
Energy Consumption Optimization of a Fluid Bed Dryer in Pharmaceutical Manufacturing Using EDA (Exploratory Data Analysis)
by Roberto Barriga, Miquel Romero, Houcine Hassan and David F. Nettleton
Sensors 2023, 23(8), 3994; https://doi.org/10.3390/s23083994 - 14 Apr 2023
Viewed by 2559
Abstract
In this paper, a data preprocessing methodology, EDA (Exploratory Data Analysis), is used for performing an exploration of the data captured from the sensors of a fluid bed dryer to reduce the energy consumption during the preheating phase. The objective of this process [...] Read more.
In this paper, a data preprocessing methodology, EDA (Exploratory Data Analysis), is used for performing an exploration of the data captured from the sensors of a fluid bed dryer to reduce the energy consumption during the preheating phase. The objective of this process is the extraction of liquids such as water through the injection of dry and hot air. The time taken to dry a pharmaceutical product is typically uniform, independent of the product weight (Kg) or the type of product. However, the time it takes to heat up the equipment before drying can vary depending on different factors, such as the skill level of the person operating the machine. EDA (Exploratory Data Analysis) is a method of evaluating or comprehending sensor data to derive insights and key characteristics. EDA is a critical component of any data science or machine learning process. The exploration and analysis of the sensor data from experimental trials has facilitated the identification of an optimal configuration, with an average reduction in preheating time of one hour. For each processed batch of 150 kg in the fluid bed dryer, this translates into an energy saving of around 18.5 kWh, giving an annual energy saving of over 3.700 kWh. Full article
(This article belongs to the Special Issue Smart Sensor and Digital Twin Technologies for Industrial Process Control and Monitoring)
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11 pages, 1077 KiB  
Article
Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture
by Martí de Castro-Cros, Manel Velasco and Cecilio Angulo
Sensors 2023, 23(3), 1236; https://doi.org/10.3390/s23031236 - 21 Jan 2023
Cited by 3 | Viewed by 1944
Abstract
Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. [...] Read more.
Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance. Full article
(This article belongs to the Special Issue Smart Sensor and Digital Twin Technologies for Industrial Process Control and Monitoring)
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Review

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30 pages, 4532 KiB  
Review
A Literature Review on the Development and Creation of Digital Twins, Cyber-Physical Systems, and Product-Service Systems
by Michel Fett, Fabian Wilking, Stefan Goetz, Eckhard Kirchner and Sandro Wartzack
Sensors 2023, 23(24), 9786; https://doi.org/10.3390/s23249786 - 12 Dec 2023
Cited by 7 | Viewed by 2666
Abstract
Digital Twins offer vast potential, yet many companies, particularly small and medium-sized enterprises, hesitate to implement them. This hesitation stems partly from the challenges posed by the interdisciplinary nature of creating Digital Twins. To address these challenges, this paper explores systematic approaches for [...] Read more.
Digital Twins offer vast potential, yet many companies, particularly small and medium-sized enterprises, hesitate to implement them. This hesitation stems partly from the challenges posed by the interdisciplinary nature of creating Digital Twins. To address these challenges, this paper explores systematic approaches for the development and creation of Digital Twins, drawing on relevant methods and approaches presented in the literature. Conducting a systematic literature review, we delve into the development of Digital Twins while also considering analogous concepts, such as Cyber-Physical Systems and Product-Service Systems. The compiled literature is categorised into three main sections: holistic approaches, architecture, and models. Each category encompasses various subcategories, all of which are detailed in this paper. Through this comprehensive review, we discuss the findings and identify research gaps, shedding light on the current state of knowledge in the field of Digital Twin development. This paper aims to provide valuable insights for practitioners and researchers alike, guiding them in navigating the complexities associated with the implementation of Digital Twins. Full article
(This article belongs to the Special Issue Smart Sensor and Digital Twin Technologies for Industrial Process Control and Monitoring)
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