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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 27571

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Special Issue Editors

Dr. Min Xia

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Guest Editor

Department of Engineering, Lancaster University, Lancaster LA1 4YW, UK
Interests: machine condition monitoring; smartning machine condition monitoring; smart sensing; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Xiangcheng Chen

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Guest Editor

School of Automation, Wuhan University of Technology, Wuhan, China
Interests: computer vision; machine perception; optical measurement

Dr. Haoxiang Lang

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Guest Editor

Department of Automotive and Mechatronics Engineering, University of Ontario Institute of Technology, Oshawa, ON, Canada
Interests: mechatronics; autonomous robots; artificial intelligence; machine learning

Dr. Haidong Shao

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Guest Editor

College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
Interests: intelligent fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Prof. Darren Williams

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Guest Editor

The Welding Institute, Cambridge, UK
Interests: digital manufacturing; Industry 4.0; welding and joining technologies

Special Issue Information

Dear Colleagues,

In the emerging Industry 4.0, digitalization and intelligence are the two crucial enabling technologies. The digital revolution has radically changed traditional industrial processes, from new digital model-based engineering to smart factories. With the rapid development of next-generation information and communication technologies (ICT), the huge number of data generated in the industrial processes provide great potential in achieving improved decision-making in all the stages, such as design, manufacturing, operation and maintenance, remanufacturing, and recycling. With advanced sensing, data mining, and Artificial Intelligence (AI), especially deep learning, critical patterns and trends can be recognized or predicted, leading to accurate monitoring, control, and optimization of the processes. The realization of the smart sensing, monitoring, and control of industrial processes still needs further research efforts in the areas of innovative sensor design with high accuracy and reliability; advanced signal processing and sensor fusion techniques with multi-modal data; intelligent condition monitoring with unlabeled and unbalanced condition data; machine-learning-based intelligent control methodologies; the enhancement of the sensing–monitoring–control cycle; and data security in critical industrial applications.

This Special Issue aims to cover the state of the art and advancements in smart sensing, monitoring, and control in industrial applications, together with emerging standards and research topics that would push forward the realization of Industry 4.0.

Dr. Min Xia
Dr. Xiangcheng Chen
Dr. Haoxiang Lang
Dr. Haidong Shao
Prof. Darren Williams
Guest Editors

Manuscript Submission Information

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Keywords

Smart sensors
Intelligent condition monitoring
Deep learning
Industrial big data mining
Data security
Machine learning control

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Published Papers (11 papers)

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Research

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22 pages, 16153 KiB

Open AccessArticle

Real Time Assessment of Smart Concrete Inspection with Piezoelectric Sensors

by Tan Kai Noel Quah, Tran Vy Khanh Vo, Yi Wei Daniel Tay, Ming Jen Tan, Teck Neng Wong and King Ho Holden Li

Electronics 2023, 12(18), 3762; https://doi.org/10.3390/electronics12183762 - 6 Sep 2023

Cited by 5 | Viewed by 1593

Abstract

Utilization of an Electromechanical impedance (EMI) technique with Piezoelectric (PZT) sensors has showed potential for Structural Health Monitoring (SHM). The changes in mechanical structure via flexural bending and cracking can be detected by monitoring the deviations in electrical impedance signals recorded with embedded PZT sensors. This paper has conducted a comprehensive study on the potential of an EMI technique with embedded PZT sensors with 3D Concrete Printing (3DCP) structures subjected to flexural bending test until plastic failure. The impact of different Piezoelectric housing methods and materials has been studied comprehensively through the monitoring of EMI signals. Experimental results indicate that material housing types and thickness affect the sensitivity of EMI readings but also performed as a reinforcement when a load is directly applied. The embedded PZT sensors with the EMI technique have shown strong potential to address the cost and lifecycle challenges posed by traditional construction methods as the insertion of PZT sensors seamlessly functions with 3DCP workflows. Further developmental work can be carried out to address the sensitivity of the sensor, performance as a reinforcement, and installation automation. The results proved that the coated sensors could detect fractures in 3DCP concrete with decreased sensitivity on thicker coating layers through the variance in materials and coating thickness in the paper. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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19 pages, 8680 KiB

Open AccessArticle

Denying Evolution Resampling: An Improved Method for Feature Selection on Imbalanced Data

by Li Quan, Tao Gong and Kaida Jiang

Electronics 2023, 12(15), 3212; https://doi.org/10.3390/electronics12153212 - 25 Jul 2023

Viewed by 1256

Abstract

Imbalanced data classification is an important problem in the field of computer science. Traditional classification algorithms often experience a decrease in accuracy when the data distribution is uneven. Therefore, measures need to be taken to improve the balance of the dataset and enhance the classification accuracy of the model. We have designed a data resampling method to improve the accuracy of classification detection. This method relies on the negative selection process to constrain the data evolution process. By combining the CRITIC method with regression coefficients, we establish crossover selection probabilities for elite genes to achieve an evolutionary resampling process. Based on independent weights, the feature analysis improves by 3%. We evaluated the resampled results on publicly available datasets using traditional logistic regression with cross-validation. Compared to the other resampling models, the F1 score performance of the logistic regression five-fold cross-validation is more stable than the other methods using the two sampling results of the proposed method. The effectiveness of the proposed method is verified based on F1 score evaluation results. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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19 pages, 12365 KiB

Open AccessArticle

Smooth Coverage Path Planning for UAVs with Model Predictive Control Trajectory Tracking

by Paolo Tripicchio, Matteo Unetti, Salvatore D’Avella and Carlo Alberto Avizzano

Electronics 2023, 12(10), 2310; https://doi.org/10.3390/electronics12102310 - 19 May 2023

Cited by 5 | Viewed by 2063

Abstract

Within the Industry 4.0 ecosystem, Inspection Robotics is one fundamental technology to speed up monitoring processes and obtain good accuracy and performance of the inspections while avoiding possible safety issues for human personnel. This manuscript investigates the robotics inspection of areas and surfaces employing Unmanned Aerial Vehicles (UAVs). The contribution starts by addressing the problem of coverage path planning and proposes a smoothing approach intended to reduce both flight time and memory consumption to store the target navigation path. Evaluation tests are conducted on a quadrotor equipped with a Model Predictive Control (MPC) policy and a Simultaneous Localization and Mapping (SLAM) algorithm to localize the UAV in the environment. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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23 pages, 10415 KiB

Open AccessArticle

An Edge Intelligent Method for Bearing Fault Diagnosis Based on a Parameter Transplantation Convolutional Neural Network

by Xiang Ding, Hang Wang, Zheng Cao, Xianzeng Liu, Yongbin Liu and Zhifu Huang

Electronics 2023, 12(8), 1816; https://doi.org/10.3390/electronics12081816 - 11 Apr 2023

Cited by 16 | Viewed by 2257

Abstract

A bearing is a key component in rotating machinery. The prompt monitoring of a bearings’ condition is critical for the reduction of mechanical accidents. With the rapid development of artificial intelligence technology in recent years, machine learning-based intelligent fault diagnosis (IFD) methods have achieved remarkable success in the field of bearing condition monitoring. However, most algorithms are developed based on computer platforms that focus on analyzing offline, rather than real-time, signals. In this paper, an edge intelligence diagnosis method called S-AlexNet, which is based on a parameter transplantation convolutional neural network (CNN), is proposed. The method deploys the lightweight IFD method in a low-cost embedded system to monitor the bearing status in real time. Firstly, a lightweight IFD algorithm model is designed for embedded systems. The model is trained on a PC to obtain optimal parameters, such as the model’s weights and bias. Finally, the optimal parameters are transplanted into the embedded system model to identify the bearing status on the edge side. Two datasets were used to validate the performance of the proposed method. The validation using the CWRU dataset shows that the proposed method achieves an average prediction accuracy of 94.4% on the test set. The validation using self-built data shows that the proposed method can identify bearing operating status in embedded systems with an average prediction accuracy of 99.81%. The results indicate that the proposed method has the advantages of high recognition accuracy, low model complexity, low cost, and high portability, which allow for the simple and effective implementation of the edge IFD of bearings in embedded systems. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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14 pages, 2205 KiB

Open AccessFeature PaperArticle

Effective and Privacy-Preserving Estimation of the Density Distribution of LBS Users under Geo-Indistinguishability

by Jongwook Kim and Byungjin Lim

Electronics 2023, 12(4), 917; https://doi.org/10.3390/electronics12040917 - 12 Feb 2023

Cited by 3 | Viewed by 1382

Abstract

With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users’ locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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12 pages, 2683 KiB

Open AccessFeature PaperArticle

Security Access Control Method for Wind-Power-Monitoring System Based on Agile Authentication Mechanism

by Yingli Shu, Quande Yuan, Wende Ke and Lei Kou

Electronics 2022, 11(23), 3938; https://doi.org/10.3390/electronics11233938 - 28 Nov 2022

Cited by 3 | Viewed by 1404

Abstract

With the continuous increase in the proportion of wind power construction and grid connection, the deployment scale of state sensors in wind-power-monitoring systems has grown rapidly with an aim on the problems that the communication authentication process between the wind turbine status sensor and the monitoring gateway is complex and the adaptability of the massive sensors is insufficient. A security access control method for a wind-power-monitoring system based on agile authentication mechanism is proposed in this paper. First, a lightweight key generation algorithm based on one-way hash function is designed. The algorithm realizes fixed-length compression and encryption of measurement data of any length. Under the condition of ensuring security, the calculation and communication cost in the later stage of authentication are effectively reduced. Second, to reduce the redundant process of wind turbine status sensor authentication, an agile authentication model of wind turbine status sensor based on a lightweight key is constructed. Constrained by the reverse order extraction of key information in the lightweight keychain, the model can realize lightweight communication between massive wind turbine status sensors and regional gateways. Finally, the proposed method is compared and verified using the wind turbine detection data set provided by the National New Energy Laboratory of the United States. The experimental results show that this method can effectively reduce the certification cost of a wind-power-monitoring system. Additionally, it can improve the efficiency of status sensor identity authentication and realize the agility and efficiency of the authentication process. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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10 pages, 3004 KiB

Open AccessArticle

Chatter Detection in Variable Cutting Depth Side Milling Using VMD and Vibration Characteristics Analysis

by Na Zhao, Yingxin Su, Shijuan Wang, Min Xia and Changfu Liu

Electronics 2022, 11(22), 3779; https://doi.org/10.3390/electronics11223779 - 17 Nov 2022

Cited by 3 | Viewed by 1645

Abstract

Chatter is a key factor affecting tool wear, workpiece surface quality and cutting efficiency. When milling thin-walled parts, it is difficult to extract the chatter frequency band due to the time-varying characteristics of the dynamic parameters of the machining system. Variational mode decomposition (VMD) shows good performance in signal processing, but the decomposition result of this algorithm is limited by the influence of initialization parameters. Therefore, this paper proposes a scheme to determine the number of VMD decomposition layers based on the number of Fourier transform frequency peaks. The feasibility of the scheme is verified by the simulation signal and experiment signal. The results show that taking the peak number of the spectrum as the decomposition level of VMD, the spectrum distribution of the decomposed intrinsic mode function (IMF) is clear, and the frequency band containing rich chatter information can be effectively extracted. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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16 pages, 6862 KiB

Open AccessArticle

Dynamic Modeling and Simulation of a Four-Wheel Skid-Steer Mobile Robot Using Linear Graphs

by Eric McCormick, Haoxiang Lang and Clarence W. de Silva

Electronics 2022, 11(15), 2453; https://doi.org/10.3390/electronics11152453 - 6 Aug 2022

Cited by 5 | Viewed by 3558

Abstract

This paper presents the application of the concepts and approaches of linear graph (LG) theory in the modeling and simulation of a four-wheel skid-steer mobile robotic system. An LG representation of the system is proposed, and the accompanying state-space model of the dynamics of a mobile robot system is evaluated using the associated LGtheory MATLAB toolbox, which was developed in our lab. A genetic algorithm (GA)-based parameter estimation method is employed to determine the system parameters, which leads to a very accurate simulation of the model. The developed model is then evaluated and validated by comparing the simulated LG model trajectory with the trajectory of an ROS Gazebo-simulated robot and experimental data obtained from the physical robotic system. The obtained results demonstrate that the proposed LG model, combined with the GA parameter estimation process, produces a highly accurate method of modeling and simulating a mobile robotic system. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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13 pages, 30920 KiB

Open AccessArticle

Features and Always-On Wake-Up Detectors for Sparse Acoustic Event Detection

by Marko Gazivoda, Dinko Oletić and Vedran Bilas

Electronics 2022, 11(3), 478; https://doi.org/10.3390/electronics11030478 - 6 Feb 2022

Cited by 6 | Viewed by 2347

Abstract

The need to understand and manage our surroundings has led to increased interest in sensor networks for the continuous monitoring of events and processes of interest. To reduce the power consumption required for continuous monitoring, dedicated always-on wake-up detectors have been designed, with an emphasis on their low power consumption, simple and robust design, and reliable and accurate detection. An especially interesting application of these wake-up detectors is in detecting acoustic signals. In this paper, we present a study on the features and detectors applicable for the detection of sporadic acoustic events. We perform a state-of-the-art acoustic detector analysis, grouping the detectors based on the features they utilize and their implementations. This analysis shows that acoustic wake-up detectors predominantly utilize spectro-temporal (56%) and temporal features (36%). Following the state-of-the-art analysis, we select two detector architecture candidates for a case study on passing motor vehicle detection. We utilize our previously developed spectro-temporal decomposition detector and develop a novel level-crossing rate detector. The results of the case study shows that the proposed level-crossing rate detector has lower component count (44 compared to 70) and power consumption (9.1 µW compared to 34.6 µW) and is an optimal solution for SNRs over 0 dB. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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20 pages, 10507 KiB

Open AccessFeature PaperArticle

Analysis of Complex Solid-Gas Flow under the Influence of Gravity through Inclined Channel and Comparison with Real-Time Dual-Sensor System

by Usama Abrar, Adnan Yousaf, Nasif Raza Jaffri, Ateeq Ur Rehman, Aftab Ahmad, Akber Abid Gardezi, Salman Naseer, Muhammad Shafiq and Jin-Ghoo Choi

Electronics 2021, 10(22), 2849; https://doi.org/10.3390/electronics10222849 - 19 Nov 2021

Cited by 2 | Viewed by 2064

Abstract

Gas-solid flow is used in the chemical industry, food industry, pharmaceuticals, vehicles, and power generation. The calculation of flow has aroused great interest in contemporary industry. In recent decades, researchers have been seeking to build an effective system to monitor and calculate gas-solid flow. Attempts have been extended from computational modeling to the creation of flow pattern visualization methods and mass flow (MFR) quantification. MFR is usually studied by volume flow concentration (VFC) and velocity distribution of solid particles. A non-invasive device is used for testing MFR, in which electronic and mechanical sensors are used to balance the shortcomings related to each other. This study investigates the simulation of flow patterns to demonstrate the behavior of solid particles as they pass through the channel. The particles are allowed to slide longitudinally in the insulated tending channel. This slippage is due to the influence of natural gravity. Electronic sensor components are used to measure the velocity distribution and concentration of volumetric flow. The load cell is used as an auxiliary sensor for measuring MFR. In addition, ANSYS fluent is used to analyze streaming queries. The experimental results are related to evaluating the accuracy and relative error of the data collected from various sensors under different conditions. However, the simulation results can help explain the movement of the gas-solid mixture and can understand the cause of pipeline blockage during the slow movement of solid particles. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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Review

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22 pages, 3057 KiB

Open AccessReview

Prospects of Wireless Energy-Aware Sensors for Smart Factories in the Industry 4.0 Era

by Olfa Kanoun, Sabrine Khriji, Slim Naifar, Sonia Bradai, Ghada Bouattour, Ayda Bouhamed, Dhouha El Houssaini and Christian Viehweger

Electronics 2021, 10(23), 2929; https://doi.org/10.3390/electronics10232929 - 26 Nov 2021

Cited by 26 | Viewed by 5418

Abstract

Advanced sensors are becoming essential for modern factories, as they contribute by gathering comprehensive data about machines, processes, and human-machine interaction. They play an important role in improving manufacturing performance, in-factory logistics, predictive maintenance, supply chains, and digitalization in general. Wireless sensors and wireless sensor networks (WSNs) provide, in this context, significant advantages as they are flexible and easily deployable. They have reduced installation and maintenance costs and contributed by reducing cables and preinstalled infrastructure, leading to improved reliability. WSNs can be retrofitted in machines to provide direct information from inside the processes. Recent developments have revealed exciting possibilities to enhance energy harvesting (EH) and wireless energy transmission, enabling a reliable use of wireless sensors in smart factories. This review provides an overview of the potential of energy aware WSNs for industrial applications and shows relevant techniques for realizing a sustainable energy supply based on energy harvesting and energy transfer. The focus is on high-performance converter solutions and improvement of frequency, bandwidth, hybridization of the converters, and the newest trends towards flexible converters. We report on possibilities to reduce the energy consumption in wireless communication on the node level and on the network level, enabling boosting network efficiency and operability. Based on the existing technologies, energy aware WSNs can nowadays be realized for many applications in smart factories. It can be expected that they will play a great role in the future as an enabler for digitalization in this decisive economic sector. Full article

(This article belongs to the Special Issue Smart Sensing, Monitoring, and Control in Industry 4.0)

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