Articles | Volume 7, issue 1
https://doi.org/10.5194/jsss-7-359-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/jsss-7-359-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sensors 4.0 – smart sensors and measurement technology enable Industry 4.0
Andreas Schütze
CORRESPONDING AUTHOR
Lab for Measurement Technology, Department Systems Engineering,
Saarland University, 66123 Saarbruecken, Germany
Centre for Mechatronics and Automation Technology (ZeMA gGmbH),
66121 Saarbruecken, Germany
Nikolai Helwig
Centre for Mechatronics and Automation Technology (ZeMA gGmbH),
66121 Saarbruecken, Germany
Tizian Schneider
Centre for Mechatronics and Automation Technology (ZeMA gGmbH),
66121 Saarbruecken, Germany
Viewed
Total article views: 10,558 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 May 2018)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
6,533 | 3,886 | 139 | 10,558 | 157 | 128 |
- HTML: 6,533
- PDF: 3,886
- XML: 139
- Total: 10,558
- BibTeX: 157
- EndNote: 128
Viewed (geographical distribution)
Total article views: 9,302 (including HTML, PDF, and XML)
Thereof 9,242 with geography defined
and 60 with unknown origin.
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
104 citations as recorded by crossref.
- Flexure-based torque and thrust force drilling dynamometer with Hall effect sensor displacement measurement R. Zameroski et al. 10.1016/j.cirp.2024.04.086
- A critical review of future aspects of digitalization next generation Li-ion batteries manufacturing process P. Dammala et al. 10.1016/j.est.2023.109209
- All-Around Approach for Reliability of Measurement Data in the Industry 4.0 G. D'Emilia et al. 10.1109/MIM.2021.9345650
- Competencies for Industry 4.0 M. Hernandez-de-Menendez et al. 10.1007/s12008-020-00716-2
- Deep Fusion of Intelligent Meridian Sensing Technology and Huoluo Xiaoling Pills in the Treatment of Knee Osteoarthritis J. Sun et al. 10.1155/2022/8043674
- Self-Powered Sensors: Applications, Challenges, and Solutions S. Javaid et al. 10.1109/JSEN.2023.3241947
- Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis L. Kipper et al. 10.1080/00207543.2019.1671625
- Numerical and Experimental Performance Analysis of the Chirped Fiber Bragg Grating Based Abrasion Sensor for the Maintenance Applications in the Industry 4.0 K. Markowski et al. 10.3390/s20030770
- Design and implementation of smart pressure sensor for automotive applications H. Soy & İ. Toy 10.1016/j.measurement.2021.109184
- Monitoring manufacturing systems using AI: A method based on a digital factory twin to train CNNs on synthetic data M. Urgo et al. 10.1016/j.cirpj.2024.03.005
- Precision Agriculture Technologies for Crop and Livestock Production in the Czech Republic J. Vrchota et al. 10.3390/agriculture12081080
- Machine learning and internet of things in industry 4.0: A review M. Rahman et al. 10.1016/j.measen.2023.100822
- Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence K. Govindan et al. 10.1016/j.tre.2022.102725
- Magnetic focusing sensor and its characterizations of defect non-destructive testing for ferromagnetic steel plate S. Liu et al. 10.1016/j.ndteint.2024.103106
- A review of Industry 4.0 and additive manufacturing synergy M. Khorasani et al. 10.1108/RPJ-08-2021-0194
- Prediction assessment methodology for maintenance applications in manufacturing P. Aivaliotis et al. 10.1016/j.procir.2021.11.252
- Prospects and Challenges with Legal Informatics and Legal Metrology Framework in the Context of Industry 6.0 S. Chourasia et al. 10.1007/s12647-023-00664-8
- Design and Construction of Intelligent Corridor of Tourist Attractions Based on Sensor Network L. Hu & G. Sun 10.1155/2022/8366122
- A Review of Attacks, Vulnerabilities, and Defenses in Industry 4.0 with New Challenges on Data Sovereignty Ahead V. Pedreira et al. 10.3390/s21155189
- Insights from a Patent Portfolio Analysis on Sensor Technologies for Measuring Fruit Properties Ž. Kevrešan et al. 10.3390/horticulturae10010030
- Multiparameter Sensor Based on a Multi-Interferometric Serial Configuration For Temperature and Strain Measurements R. Perez-Herrera et al. 10.1109/JSTQE.2021.3072163
- Process monitoring of machining R. Teti et al. 10.1016/j.cirp.2022.05.009
- Innovative Strategy to Meet the Challenges of the Future Digital Society O. Bonnaud 10.46604/aiti.2021.6724
- High Temperature Measurement with Low Cost, VCSEL-Based, Interrogation System Using Femtosecond Bragg Gratings K. Markowski et al. 10.3390/s22249768
- A sustainable smart IoT-based solid waste management system A. Henaien et al. 10.1016/j.future.2024.03.056
- Iot based Industrial Sensor Monitoring and Alerting System using Raspberry Pi C. Shalini & I. Mr Prakash 10.1088/1757-899X/981/4/042010
- Chemical Sensing with Atomically Thin Platinum Templated by a 2D Insulator K. Kim et al. 10.1002/admi.201902104
- Platform-based manufacturing T. Tolio et al. 10.1016/j.cirp.2023.04.091
- A deep convolutional neural network for vibration-based health-monitoring of rotating machinery P. Ong et al. 10.1016/j.dajour.2023.100219
- An Architecture for Big IoT Data Analytics in the Oil and Gas Industry R. Aliguliyev et al. 10.4018/IJHIoT.2020070102
- A Review on Data-Driven Quality Prediction in the Production Process with Machine Learning for Industry 4.0 A. Md et al. 10.3390/pr10101966
- De las nanotecnologías a la industria 4.0: una evolución de términos G. Foladori & Á. Ortiz-Espinoza 10.30578/nomadas.n55a4
- Development of a wireless smart sensor system and case study on lifting risk assessment V. Selvaraj et al. 10.1016/j.mfglet.2024.09.027
- Cyber–Physiochemical Interfaces T. Wang et al. 10.1002/adma.201905522
- Comparison of different ML methods concerning prediction quality, domain adaptation and robustness P. Goodarzi et al. 10.1515/teme-2021-0129
- Smart Wireless Transducer Dedicated for Use in Aviation Laboratories T. Kabala & J. Weremczuk 10.3390/s24113585
- An approach enabling Accuracy-as-a-Service for resistance-based sensors using intelligent Digital Twins V. Stegmaier et al. 10.1016/j.procir.2022.05.071
- Responsiveness of the Sensor Network to Alarm Events Based on the Potts Model A. Paszkiewicz & J. Węgrzyn 10.3390/s20236979
- Industrial condition monitoring with smart sensors using automated feature extraction and selection T. Schneider et al. 10.1088/1361-6501/aad1d4
- Significance and implications of digital transformation in metrology in India N. Garg et al. 10.1016/j.measen.2021.100248
- Testing Thermostatic Bath End-Scale Stability for Calibration Performance with a Multiple-Sensor Ensemble Using ARIMA, Temporal Stochastics and a Quantum Walker Algorithm G. Besseris 10.3390/s23042267
- Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective S. Kang et al. 10.1007/s10845-021-01817-9
- Early Warning Systems in Industry 4.0: A Bibliometric and Topic Analysis T. Bertoncel & M. Meško 10.4018/IJESMA.2019040104
- Optimization of 5G Networks for Smart Logistics E. Khatib & R. Barco 10.3390/en14061758
- IoT technology proposal for multi-adaptative sensing integrated into data science and analytics scenarios C. Junior et al. 10.1016/j.procs.2022.11.155
- Konzept zur Sensornachrüstung D. Panick & M. Marré 10.1515/zwf-2022-1162
- Setting process control chart limits for rounded-off measurements R. Etgar & S. Freund 10.1016/j.heliyon.2023.e13655
- I4.0I: A New Way to Rank How Involved a Company Is in the Industry 4.0 Era V. Zilli et al. 10.3390/fi15020073
- Virtual Reality Technology in Landscape Design at the Exit of Rail Transit Using Smart Sensors C. Sun et al. 10.1155/2022/6519605
- Developing an I4.0 Cyber-Physical System to Enhance Efficiency and Competitiveness in Manufacturing F. Jamil et al. 10.3390/app13169333
- A Markov chain model for IEEE 802.15.4 in time critical wireless sensor networks under periodic traffic with reneging packets H. Hadadian Nejad Yousefi et al. 10.1007/s12652-021-02984-6
- A monitoring framework based on exergetic analysis for sustainability assessment of direct laser metal deposition process V. Selicati et al. 10.1007/s00170-021-08177-x
- Spectrum analysis of moving average operator and construction of time-frequency hybrid sequence operator C. Lin et al. 10.1108/GS-09-2020-0128
- Reliability Assessment of MV Power Connections P. Hoduń & M. Borecki 10.3390/en14216965
- An intelligent data capturing framework to improve condition monitoring and anomaly detection for industrial machines S. Robyns et al. 10.1016/j.procs.2022.12.267
- A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories M. Zribi et al. 10.1016/j.eswa.2024.124892
- IoT based Earthquake and Hazardous Gases Detection in Coal Mines using Raspberry Pi G. Ravitheja & K. Jeevana Jyothi 10.1088/1757-899X/981/4/042011
- Adoption paths of digital transformation in manufacturing SME E. Battistoni et al. 10.1016/j.ijpe.2022.108675
- Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models C. König & A. Helmi 10.3390/s20113307
- Sensor Technologies for Hydraulic Valve and System Performance Monitoring: Challenges and Perspectives J. Liu et al. 10.1002/adsr.202300130
- Real-Time Fault Detection and Condition Monitoring for Industrial Autonomous Transfer Vehicles Utilizing Edge Artificial Intelligence Ö. Gültekin et al. 10.3390/s22093208
- Characterization of a smart transducer for axial force measurements in vibrating environments S. Naifar et al. 10.1016/j.measurement.2020.108157
- Integrating metrological principles into the Internet of Things: a digital maturity model for sensor network metrology S. Eichstädt et al. 10.1515/teme-2023-0103
- Thermal segment microwell plate control for automated liquid handling setups S. Seidel et al. 10.1039/D3LC00714F
- In-process, real-time monitoring of forming forces in rotary draw bending process X. He et al. 10.1007/s00170-024-14370-5
- Wireless Communication System and Its Application in Big Data Remote Monitoring and Decision-Making H. Kou & Z. Yang 10.1155/2022/8161917
- Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis G. Li et al. 10.1016/j.jmsy.2021.07.007
- Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines N. Kousi et al. 10.3390/app11104620
- Potential Role of Metrology in Digital Transformation for Quality Infrastructure P. Verma 10.1007/s12647-023-00716-z
- Universal Programmable Portable Measurement Device for Diagnostics and Monitoring of Industrial Fluid Power Systems R. Dindorf & P. Wos 10.3390/s21103440
- Recent Advances in Smart Organic Sensors for Environmental Monitoring Systems H. Song et al. 10.1021/acsaelm.2c01315
- Financial Early Warning Model for Listed Companies Based on the Smart Sensor Data Network Y. Wang & G. Sun 10.1155/2022/7666354
- Development, Characterisation and High-Temperature Suitability of Thin-Film Strain Gauges Directly Deposited with a New Sputter Coating System D. Klaas et al. 10.3390/s20113294
- Analysis of Uncertainties and Levels of Foreknowledge in Relation to Major Features of Emerging Technologies—The Context of Foresight Research for the Fourth Industrial Revolution A. Magruk 10.3390/su13179890
- Sensors 4.0 – smart sensors and measurement technology enable Industry 4.0 A. Schütze et al. 10.5194/jsss-7-359-2018
- La relación capital-trabajo en la Industria 4.0 G. Foladori & Á. Ortiz-Espinoza 10.17141/iconos.73.2022.5198
- A Supply-Voltage Driving Scheme for Grounded Capacitive Sensor Front-Ends M. Haberman et al. 10.1109/TIM.2022.3205648
- Detection and Location of Earth Fault in MV Feeders Using Screen Earthing Current Measurements K. Lowczowski et al. 10.3390/en13051293
- Asymmetry Considerations in Constructing Control Charts: When Symmetry Is Not the Norm R. Etgar 10.3390/sym16070811
- Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities S. Mian et al. 10.3390/su12156100
- Self-powered sensing systems with learning capability A. Alagumalai et al. 10.1016/j.joule.2022.06.001
- Influence of synchronization within a sensor network on machine learning results T. Dorst et al. 10.5194/jsss-10-233-2021
- Assessment of quality predictions achieved with machine learning using established measurement process capability procedures in manufacturing S. Schorr et al. 10.1515/teme-2021-0125
- UNVEILING TECHNICAL ASPECTS OF THE KYPS SYSTEM IN THE HOSPITALITY SECTOR OF UKRAINE A. Andrushko 10.23939/cds2024.02.092
- Bidirectional Recurrent Neural Network-Based Chemical Process Fault Diagnosis S. Zhang et al. 10.1021/acs.iecr.9b05885
- The Significance of Industry 4.0 Technologies in Enhancing Various Unit Operations Applied in the Food Sector: Focus on Food Drying A. Hassoun et al. 10.1007/s11947-024-03465-2
- Connecting circular economy and industry 4.0 S. Rajput & S. Singh 10.1016/j.ijinfomgt.2019.03.002
- Strain Sensing Technology to Enable Next-Generation Industry and Smart Machines for the Factories of the Future: A Review Y. Hamed et al. 10.1109/JSEN.2023.3313013
- Sensors Data Analysis in Supervisory Control and Data Acquisition (SCADA) Systems to Foresee Failures with an Undetermined Origin F. Maseda et al. 10.3390/s21082762
- Quality 4.0 – an evolution of Six Sigma DMAIC C. Escobar et al. 10.1108/IJLSS-05-2021-0091
- Strength enhancement and retention in magnesium subjected to uniaxial compression using centralized partial drill holes A. Krishnan & M. Gupta 10.1088/2631-8695/acd98b
- A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision M. Silva et al. 10.3390/s18103298
- Uncertainty-aware data pipeline of calibrated MEMS sensors used for machine learning T. Dorst et al. 10.1016/j.measen.2022.100376
- Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data I. Ahmad et al. 10.3390/pr8020243
- Machine learning-assisted self-powered intelligent sensing systems based on triboelectricity Z. Tian et al. 10.1016/j.nanoen.2023.108559
- Improving Virtual Sensor Models by Censored Online Data S. Luftensteiner & M. Zwick 10.1016/j.procs.2022.12.291
- Industry 4.0 Model for circular economy and cleaner production S. Rajput & S. Singh 10.1016/j.jclepro.2020.123853
- Challenges, limitations, and measurement strategies to ensure data quality in deep-sea sensors A. Skålvik et al. 10.3389/fmars.2023.1152236
- Gathering Expert Knowledge in Process Industry S. Luftensteiner et al. 10.1016/j.procs.2022.12.293
- Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications A. Varshney et al. 10.1007/s12647-021-00453-1
- Enabling flexible manufacturing system (FMS) through the applications of industry 4.0 technologies M. Javaid et al. 10.1016/j.iotcps.2022.05.005
- Impact of industry 4.0 to create advancements in orthopaedics M. Javaid & A. Haleem 10.1016/j.jcot.2020.03.006
- Significance of sensors for industry 4.0: Roles, capabilities, and applications M. Javaid et al. 10.1016/j.sintl.2021.100110
- AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task F. Zoghlami et al. 10.3390/s21134405
Latest update: 14 Dec 2024
Short summary
“Industrie 4.0” or the Industrial Internet of Things (IIoT) describe the current (r)evolution in industrial automation and control. This is fundamentally based on smart sensors, which generate data and allow further functionality from self-monitoring and self-configuration to condition monitoring of complex processes. The paper reviews the development of sensor technology over the last 2 centuries and highlights some of the potential that can be achieved with smart sensors and data analysis.
“Industrie 4.0” or the Industrial Internet of Things (IIoT) describe the current...
Special issue