Research

15 pages, 5034 KiB

Open AccessArticle

Identification of Aluminothermic Reaction and Molten Aluminum Level through Vision System

by Yuvan Sathya Ravi, Fabio Conti, Paolo Fasoli, Emanuele Della Bosca, Maurizio Colombo, Andrea Mazzoleni and Marco Tarabini

Sensors 2023, 23(12), 5506; https://doi.org/10.3390/s23125506 - 12 Jun 2023

Viewed by 940

Abstract

During the secondary production of aluminum, upon melting the scrap in a furnace, there is the possibility of developing an aluminothermic reaction, which produces oxides in the molten metal bath. Aluminum oxides must be identified and removed from the bath, as they modify [...] Read more.

During the secondary production of aluminum, upon melting the scrap in a furnace, there is the possibility of developing an aluminothermic reaction, which produces oxides in the molten metal bath. Aluminum oxides must be identified and removed from the bath, as they modify the chemical composition and reduce the purity of the product. Furthermore, accurate measurement of molten aluminum level in a casting furnace is crucial to obtain an optimal liquid metal flow rate which influences the final product quality and process efficiency. This paper proposes methods for the identification of aluminothermic reactions and molten aluminum levels in aluminum furnaces. An RGB Camera was used to acquire video from the furnace interior, and computer vision algorithms were developed to identify the aluminothermic reaction and melt level. The algorithms were developed to process the image frames of video acquired from the furnace. Results showed that the proposed system allowed the online identification of the aluminothermic reaction and the molten aluminum level present inside the furnace at a computation time of 0.7 s and 0.4 s per frame, respectively. The advantages and limitations of the different algorithms are presented and discussed. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

21 pages, 2854 KiB

Open AccessArticle

ECG-Based Stress Detection and Productivity Factors Monitoring: The Real-Time Production Factory System

by Massimiliano Donati, Martina Olivelli, Romano Giovannini and Luca Fanucci

Sensors 2023, 23(12), 5502; https://doi.org/10.3390/s23125502 - 11 Jun 2023

Cited by 2 | Viewed by 1804

Abstract

Productivity and production quality have become primary goals for the success of companies in all industrial and manufacturing sectors. Performance in terms of productivity is influenced by several factors including machinery efficiency, work environment and safety conditions, production processes organization, and aspects related [...] Read more.

Productivity and production quality have become primary goals for the success of companies in all industrial and manufacturing sectors. Performance in terms of productivity is influenced by several factors including machinery efficiency, work environment and safety conditions, production processes organization, and aspects related to workers’ behavior (human factors). In particular, work-related stress is among the human factors that are most impactful and difficult to capture. Thus, optimizing productivity and quality in an effective way requires considering all these factors simultaneously. The proposed system aims to detect workers’ stress and fatigue in real time using wearable sensors and machine learning techniques and also integrate all data regarding the monitoring of production processes and the work environment into a single platform. This allows comprehensive multidimensional data analysis and correlation research, enabling organizations to improve productivity through appropriate work environments and sustainable processes for workers. The on-field trial demonstrated the technical and operational feasibility of the system, its high degree of usability, and the ability to detect stress from ECG signals exploiting a 1D Convolutional Neural Network (accuracy 88.4%, F1-score 0.90). Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

20 pages, 7502 KiB

Open AccessArticle

IoT System for Real-Time Posture Asymmetry Detection

by Monica La Mura, Marco De Gregorio, Patrizia Lamberti and Vincenzo Tucci

Sensors 2023, 23(10), 4830; https://doi.org/10.3390/s23104830 - 17 May 2023

Cited by 1 | Viewed by 1605

Abstract

The rise of the Internet of Things (IoT) has enabled the development of measurement systems dedicated to preventing health issues and monitoring conditions in smart homes and workplaces. IoT systems can support monitoring people doing computer-based work and avoid the insurgence of common [...] Read more.

The rise of the Internet of Things (IoT) has enabled the development of measurement systems dedicated to preventing health issues and monitoring conditions in smart homes and workplaces. IoT systems can support monitoring people doing computer-based work and avoid the insurgence of common musculoskeletal disorders related to the persistence of incorrect sitting postures during work hours. This work proposes a low-cost IoT measurement system for monitoring the sitting posture symmetry and generating a visual alert to warn the worker when an asymmetric position is detected. The system employs four force sensing resistors (FSR) embedded in a cushion and a microcontroller-based read-out circuit for monitoring the pressure exerted on the chair seat. Java-based software performs the real-time monitoring of the sensors’ measurements and implements an uncertainty-driven asymmetry detection algorithm. The shifts from a symmetric to an asymmetric posture and vice versa generate and close a pop-up warning message, respectively. In this way, the user is promptly notified when an asymmetric posture is detected and invited to adjust the sitting position. Every position shift is recorded in a web database for further analysis of the sitting behavior. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

34 pages, 2554 KiB

Open AccessArticle

Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data

by Fabian Hölzke, Hagen Borstell, Frank Golatowski and Christian Haubelt

Sensors 2023, 23(10), 4744; https://doi.org/10.3390/s23104744 - 14 May 2023

Cited by 1 | Viewed by 1350

Abstract

Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the [...] Read more.

Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the application in real-world industrial contexts. In this paper, we present a method that produces an estimate of the current positioning error for each user stride. To accomplish this, we construct a virtual stride vector from Ultra-Wideband (UWB) position measurements. The virtual vectors are then compared to stride vectors from a foot-mounted Inertial Measurement Unit (IMU). Using these independent measurements, we estimate the current reliability of the UWB measurements. Positioning errors are mitigated through loosely coupled filtering of both vector types. We evaluate our method in three environments, showing that it improves positioning accuracy, especially in challenging conditions with obstructed line of sight and sparse UWB infrastructure. Additionally, we demonstrate the mitigation of simulated spoofing attacks on UWB positioning. Our findings indicate that positioning quality can be judged at runtime by comparing user strides reconstructed from UWB and IMU measurements. Our method is independent of situation- or environment-specific parameter tuning, and as such represents a promising approach for detecting both known and unknown positioning error states. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

16 pages, 5261 KiB

Open AccessArticle

A Smart Agricultural System Based on PLC and a Cloud Computing Web Application Using LoRa and LoRaWan

by Mohamed Saban, Mostapha Bekkour, Ibtisam Amdaouch, Jaouad El Gueri, Badiaa Ait Ahmed, Mohamed Zied Chaari, Juan Ruiz-Alzola, Alfredo Rosado-Muñoz and Otman Aghzout

Sensors 2023, 23(5), 2725; https://doi.org/10.3390/s23052725 - 02 Mar 2023

Cited by 15 | Viewed by 6182

Abstract

The increasing challenges of agricultural processes and the growing demand for food globally are driving the industrial agriculture sector to adopt the concept of ‘smart farming’. Smart farming systems, with their real-time management and high level of automation, can greatly improve productivity, food [...] Read more.

The increasing challenges of agricultural processes and the growing demand for food globally are driving the industrial agriculture sector to adopt the concept of ‘smart farming’. Smart farming systems, with their real-time management and high level of automation, can greatly improve productivity, food safety, and efficiency in the agri-food supply chain. This paper presents a customized smart farming system that uses a low-cost, low-power, and wide-range wireless sensor network based on Internet of Things (IoT) and Long Range (LoRa) technologies. In this system, LoRa connectivity is integrated with existing Programmable Logic Controllers (PLCs), which are commonly used in industry and farming to control multiple processes, devices, and machinery through the Simatic

I O T 2040

. The system also includes a newly developed web-based monitoring application hosted on a cloud server, which processes data collected from the farm environment and allows for remote visualization and control of all connected devices. A Telegram bot is included for automated communication with users through this mobile messaging app. The proposed network structure has been tested, and the path loss in the wireless LoRa is evaluated. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

18 pages, 7679 KiB

Open AccessArticle

Algorithms for Vision-Based Quality Control of Circularly Symmetric Components

by Paolo Brambilla, Chiara Conese, Davide Maria Fabris, Paolo Chiariotti and Marco Tarabini

Sensors 2023, 23(5), 2539; https://doi.org/10.3390/s23052539 - 24 Feb 2023

Cited by 1 | Viewed by 1379

Abstract

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence [...] Read more.

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

21 pages, 1363 KiB

Open AccessArticle

An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments

by Mattia Antonini, Miguel Pincheira, Massimo Vecchio and Fabio Antonelli

Sensors 2023, 23(4), 2344; https://doi.org/10.3390/s23042344 - 20 Feb 2023

Cited by 16 | Viewed by 4170

Abstract

Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate corrective actions. Typically, [...] Read more.

Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate corrective actions. Typically, these analyses are conducted on servers located in data centers or the cloud. However, this approach increases system complexity and is susceptible to failure in cases where connectivity is unavailable. Furthermore, this communication restriction limits the approach’s applicability in extreme industrial environments where operating conditions affect communication and access to the system. This paper proposes and evaluates an end-to-end adaptable and configurable anomaly detection system that uses the Internet of Things (IoT), edge computing, and Tiny-MLOps methodologies in an extreme industrial environment such as submersible pumps. The system runs on an IoT sensing Kit, based on an ESP32 microcontroller and MicroPython firmware, located near the data source. The processing pipeline on the sensing device collects data, trains an anomaly detection model, and alerts an external gateway in the event of an anomaly. The anomaly detection model uses the isolation forest algorithm, which can be trained on the microcontroller in just 1.2 to 6.4 s and detect an anomaly in less than 16 milliseconds with an ensemble of 50 trees and 80 KB of RAM. Additionally, the system employs blockchain technology to provide a transparent and irrefutable repository of anomalies. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

17 pages, 5669 KiB

Open AccessArticle

Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU

by Čukić Milena, Chiara Romano, Francesca De Tommasi, Massimiliano Carassiti, Domenico Formica, Emiliano Schena and Carlo Massaroni

Sensors 2023, 23(3), 1615; https://doi.org/10.3390/s23031615 - 02 Feb 2023

Cited by 6 | Viewed by 2093

Abstract

Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart’s electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from [...] Read more.

Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart’s electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body’s surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

20 pages, 5426 KiB

Open AccessArticle

Design and Metrological Analysis of a Backlit Vision System for Surface Roughness Measurements of Turned Parts

by Alessia Baleani, Nicola Paone, Jona Gladines and Steve Vanlanduit

Sensors 2023, 23(3), 1584; https://doi.org/10.3390/s23031584 - 01 Feb 2023

Cited by 2 | Viewed by 1635

Abstract

The focus of this study is to design a backlit vision instrument capable of measuring surface roughness and to discuss its metrological performance compared to traditional measurement instruments. The instrument is a non-contact high-magnification imaging system characterized by short inspection time which opens [...] Read more.

The focus of this study is to design a backlit vision instrument capable of measuring surface roughness and to discuss its metrological performance compared to traditional measurement instruments. The instrument is a non-contact high-magnification imaging system characterized by short inspection time which opens the perspective of in-line implementation. We combined the use of the modulation transfer function to evaluate the imaging conditions of an electrically tunable lens to obtain an optimally focused image. We prepared a set of turned steel samples with different roughness in the range R_a 2.4 µm to 15.1 µm. The layout of the instrument is presented, including a discussion on how optimal imaging conditions were obtained. The paper describes the comparison performed on measurements collected with the vision system designed in this work and state-of-the-art instruments. A comparison of the results of the backlit system depends on the values of surface roughness considered; while at larger values of roughness the offset increases, the results are compatible with the ones of the stylus at lower values of roughness. In fact, the error bands are superimposed by at least 58% based on the cases analyzed. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

16 pages, 42118 KiB

Open AccessArticle

Plant-Wear: A Multi-Sensor Plant Wearable Platform for Growth and Microclimate Monitoring

by Joshua Di Tocco, Daniela Lo Presti, Carlo Massaroni, Stefano Cinti, Sara Cimini, Laura De Gara and Emiliano Schena

Sensors 2023, 23(1), 549; https://doi.org/10.3390/s23010549 - 03 Jan 2023

Cited by 6 | Viewed by 3301

Abstract

Wearable devices are widely spreading in various scenarios for monitoring different parameters related to human and recently plant health. In the context of precision agriculture, wearables have proven to be a valuable alternative to traditional measurement methods for quantitatively monitoring plant development. This [...] Read more.

Wearable devices are widely spreading in various scenarios for monitoring different parameters related to human and recently plant health. In the context of precision agriculture, wearables have proven to be a valuable alternative to traditional measurement methods for quantitatively monitoring plant development. This study proposed a multi-sensor wearable platform for monitoring the growth of plant organs (i.e., stem and fruit) and microclimate (i.e., environmental temperature—T and relative humidity—RH). The platform consists of a custom flexible strain sensor for monitoring growth when mounted on a plant and a commercial sensing unit for monitoring T and RH values of the plant surrounding. A different shape was conferred to the strain sensor according to the plant organs to be engineered. A dumbbell shape was chosen for the stem while a ring shape for the fruit. A metrological characterization was carried out to investigate the strain sensitivity of the proposed flexible sensors and then preliminary tests were performed in both indoor and outdoor scenarios to assess the platform performance. The promising results suggest that the proposed system can be considered one of the first attempts to design wearable and portable systems tailored to the specific plant organ with the potential to be used for future applications in the coming era of digital farms and precision agriculture. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

15 pages, 4634 KiB

Open AccessArticle

Plant Growth Monitoring: Design, Fabrication, and Feasibility Assessment of Wearable Sensors Based on Fiber Bragg Gratings

by Daniela Lo Presti, Joshua Di Tocco, Sara Cimini, Stefano Cinti, Carlo Massaroni, Rosaria D’Amato, Michele A. Caponero, Laura De Gara and Emiliano Schena

Sensors 2023, 23(1), 361; https://doi.org/10.3390/s23010361 - 29 Dec 2022

Cited by 3 | Viewed by 3949

Abstract

Global climate change and exponential population growth pose a challenge to agricultural outputs. In this scenario, novel techniques have been proposed to improve plant growth and increase crop yields. Wearable sensors are emerging as promising tools for the non-invasive monitoring of plant physiological [...] Read more.

Global climate change and exponential population growth pose a challenge to agricultural outputs. In this scenario, novel techniques have been proposed to improve plant growth and increase crop yields. Wearable sensors are emerging as promising tools for the non-invasive monitoring of plant physiological and microclimate parameters. Features of plant wearables, such as easy anchorage to different organs, compliance with natural surfaces, high flexibility, and biocompatibility, allow for the detection of growth without impacting the plant functions. This work proposed two wearable sensors based on fiber Bragg gratings (FBGs) within silicone matrices. The use of FBGs is motivated by their high sensitivity, multiplexing capacities, and chemical inertia. Firstly, we focused on the design and the fabrication of two plant wearables with different matrix shapes tailored to specific plant organs (i.e., tobacco stem and melon fruit). Then, we described the sensors’ metrological properties to investigate the sensitivity to strain and the influence of environmental factors, such as temperature and humidity, on the sensors’ performance. Finally, we performed experimental tests to preliminary assess the capability of the proposed sensors to monitor dimensional changes of plants in both laboratory and open field settings. The promising results will foster key actions to improve the use of this innovative technology in smart agriculture applications for increasing crop products quality, agricultural efficiency, and profits. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

22 pages, 16616 KiB

Open AccessArticle

A Self-Calibrating Localization Solution for Sport Applications with UWB Technology

by Marco Piavanini, Luca Barbieri, Mattia Brambilla, Mattia Cerutti, Simone Ercoli, Andrea Agili and Monica Nicoli

Sensors 2022, 22(23), 9363; https://doi.org/10.3390/s22239363 - 01 Dec 2022

Cited by 6 | Viewed by 1742

Abstract

This study addressed the problem of localization in an ultrawide-band (UWB) network, where the positions of both the access points and the tags needed to be estimated. We considered a fully wireless UWB localization system, comprising both software and hardware, featuring easy plug-and-play [...] Read more.

This study addressed the problem of localization in an ultrawide-band (UWB) network, where the positions of both the access points and the tags needed to be estimated. We considered a fully wireless UWB localization system, comprising both software and hardware, featuring easy plug-and-play usability for the consumer, primarily targeting sport and leisure applications. Anchor self-localization was addressed by two-way ranging, also embedding a Gauss–Newton algorithm for the estimation and compensation of antenna delays, and a modified isolation forest algorithm working with low-dimensional set of measurements for outlier identification and removal. This approach avoids time-consuming calibration procedures, and it enables accurate tag localization by the multilateration of time difference of arrival measurements. For the assessment of performance and the comparison of different algorithms, we considered an experimental campaign with data gathered by a proprietary UWB localization system. Full article

(This article belongs to the Special Issue Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Selected Papers from the 2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (12 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI