Výsledky vyhľadávania - "Machine monitoring"

Augmented Reality for Machine Monitoring in Industrial Manufacturing: A Media Comparison in Terms of Efficiency, Effectiveness, and Satisfaction

Autori: Schmitt, Thomas Viklund, Philip Sjölander, Martina a ďalší

Zdroj: IEEE Access. 13:82129-82143

Predmety: Augmented Reality, Energy dashboards, Extended Reality, Human-machine interface, Industry 4.0, Industry 5.0, Machine monitoring, Operator 4.0, Usability, Usability engineering, Energy, Energy dashboard, Human Machine Interface, Industrial manufacturing, Key factors, User Centred Product Design, Användarcentrerad produktdesign, Virtual Production Development (VPD)

Popis súboru: electronic

Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-25150
https://doi.org/10.1109/ACCESS.2025.3566442

Application of point tracking in the field of metal forming for machine and process characterisation and monitoring

Autori: Lukas Martinitz Anna Bauer Katja Holzer a ďalší

Zdroj: The International Journal of Advanced Manufacturing Technology. 137:5941-5949

Predmety: Original Article, Optical measurement, KLT algorithm, Shear cutting, Machine monitoring, ddc

Popis súboru: application/pdf

Prístupová URL adresa: https://mediatum.ub.tum.de/doc/1792231/document.pdf

Advanced Sound Detection for Construction Sites

Autori: Yigit, Damla Yilmazarslan, Nalan

Zdroj: Aachen : RWTH Aachen University 69-85 (2024). doi:10.18154/RWTH-2024-09260
Construction & Robotics, Research Driven Project : construction robotics, international M. Sc. programme, [Bd.] Vol. 7: Research Paper / SS 2024 / Sigrid Brell-Cokcan, Thomas Adams ; authors: Xiangcheng Li [und 8 weitere]

Predmety: Construction Machine Monitoring, Construction Automation, Sound Detection, Activity Recognition, Feature Extraction

Prístupová URL adresa: https://publications.rwth-aachen.de/record/994346

Condition monitoring of machines by tapping data from existing sensors and retrofitting simple energy measurement technology to existing machines

Autori: Grundmann, Andreas Schmidt, Jens Reuter, Thomas

Predmety: Sensor technology, resource efficiency, predictive maintenance, measurement data acquisition, energy optimization, hydroforming, process monitoring, machine monitoring, Sensortechnik, Ressourceneffizienz, Predictive Maintenance, Messdatenerfassung, Energieoptimierung, IHU, Prozessüberwachung, Maschinenüberwachung

Relation: 14; urn:nbn:de:bsz:14-qucosa2-882872; qucosa:88287

Dostupnosť: https://tud.qucosa.de/id/qucosa%3A88305
https://tud.qucosa.de/api/qucosa%3A88305/attachment/ATT-0/

Zustandsüberwachung von Maschinen durch Datenabgriff an bestehender Sensorik und Nachrüstung einfacher Energiemesstechnik an Bestandsmaschinen

Autori: Grundmann, Andreas Schmidt, Jens Reuter, Thomas

Predmety: Sensortechnik, Ressourceneffizienz, Predictive Maintenance, Messdatenerfassung, Energieoptimierung, IHU, Prozessüberwachung, Maschinenüberwachung, Sensor technology, resource efficiency, predictive maintenance, measurement data acquisition, energy optimization, hydroforming, process monitoring, machine monitoring

Relation: 13; urn:nbn:de:bsz:14-qucosa2-882872; qucosa:88287; urn:nbn:de:bsz:14-qucosa2-883056; qucosa:88305

Dostupnosť: https://tud.qucosa.de/id/qucosa%3A88303
https://tud.qucosa.de/api/qucosa%3A88303/attachment/ATT-0/

Identifying machine times with OPC UA for Equipment as a Service (EaaS)

Autori: Evcenko, Dimitri Heinemann, Tonja Kett, Holger Joachim a ďalší

Predmety: Equipment as a Service, OEE, EaaS, overall equipment effectiveness, machine monitoring, OPC UA

A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data

Autori: Wenhao Lu Wei Wang Xuefei Qin a ďalší

Zdroj: Applied Sciences, Vol 14, Iss 24, p 11910 (2024)

Predmety: rotating machinery, fault diagnosis, imbalanced data, online machine monitoring, lightweight neural network, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Popis súboru: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/24/11910; https://doaj.org/toc/2076-3417

Prístupová URL adresa: https://doaj.org/article/930c0300b7f84f28a6d44158b8a32c69

Development of cloud visualization a machining manufacturing system shop floor

Autori: Sulistyo, Joko Tegar Setiawan, Angga Setiyasah Toha, Isa a ďalší

Prispievatelia: Sulistyo, Joko Tegar Setiawan, Angga Setiyasah Toha, Isa a ďalší

Zdroj: Indonesian Journal of Electrical Engineering and Computer Science; Vol 33, No 2: February 2024; 1005-1015 ; 2502-4760 ; 2502-4752 ; 10.11591/ijeecs.v33.i2

Predmety: Embedded System, IoT, Manufacturing System, Informatics, Industry 4.0, Internet of things, IoT cloud application, Machine monitoring, Shop floor

Popis súboru: application/pdf

Relation: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/33923/17964; https://ijeecs.iaescore.com/index.php/IJEECS/article/view/33923

Dostupnosť: https://ijeecs.iaescore.com/index.php/IJEECS/article/view/33923
https://doi.org/10.11591/ijeecs.v33.i2.pp1005-1015

Axiomatic Design of a Test Artifact for PBF-LM Machine Capability Monitoring †

Autori: Giorgetti A. Ceccanti F. Baldi N. a ďalší

Prispievatelia: Giorgetti A. Ceccanti F. Baldi N. a ďalší

Predmety: additive manufacturing, ex situ monitoring, L-PBF, laser power bed fusion, machine monitoring, PBF-LM, test artifact

Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:001192444900001; volume:12; issue:3; journal:MACHINES; https://hdl.handle.net/11590/470232

Dostupnosť: https://hdl.handle.net/11590/470232
https://doi.org/10.3390/machines12030199
https://www.mdpi.com/2075-1702/12/3/199

CYBER-PHYSICAL SYSTEMS TECHNOLOGIES AS A KEY FACTOR IN THE PROCESS OF INDUSTRY 4.0 AND SMART MANUFACTURING DEVELOPMENT

Autori: Jarosław ZUBRZYCKI Antoni ŚWIĆ Łukasz SOBASZEK a ďalší

Zdroj: Applied Computer Science, Vol 17, Iss 4, Pp 84-99 (2021)

Predmety: iot, 0209 industrial biotechnology, 9. Industry and infrastructure, Electronic computers. Computer science, 8. Economic growth, cps, Information technology, QA75.5-76.95, 02 engineering and technology, industry 4.0, machine monitoring, T58.5-58.64

Prístupová URL adresa: https://doaj.org/article/8e1a1829b4d844e69a6abcff170e843a

Decisional DNA (DDNA) Based Machine Monitoring and Total Productive Maintenance in Industry 4.0 Framework

Autori: Shafiq, Syed Imran Sanin, Cesar Szczerbicki, Edward a ďalší

Prispievatelia: Shafiq, Syed Imran Sanin, Cesar Szczerbicki, Edward a ďalší

Zdroj: Cybernetics and Systems. 53:510-519

Predmety: 9. Industry and infrastructure, Set of Experinece Knowledge Structure (SOEKS), Sustainable Development Goals, 0202 electrical engineering, electronic engineering, information engineering, SDG 9, decisional DNA, 02 engineering and technology, machine monitoring, total productive maintenance

Condition monitoring system for machine tool auxiliaries

Autori: Gittler, Thomas Stoop, Fabian Kryscio, David a ďalší

Prispievatelia: Gittler, Thomas Stoop, Fabian Kryscio, David a ďalší

Zdroj: Procedia CIRP, 88
13th CIRP Conference on Intelligent Computation in Manufacturing Engineering

Predmety: MACHINE TOOLS AND MACHINERY (MANUFACTURING TECHNOLOGY), 0209 industrial biotechnology, Neural Networks, 0203 mechanical engineering, Manufacturing systems, Condition Monitoring, Machine learning (artificial intelligence), Machine auxiliaries, Machine Monitoring, 02 engineering and technology

Popis súboru: application/application/pdf

Prístupová URL adresa: https://www.sciencedirect.com/science/article/pii/S2212827120303814
https://www.research-collection.ethz.ch/handle/20.500.11850/438683
http://hdl.handle.net/20.500.11850/438683

INDUSTRIAL DEVICE INTEGRATION AND VIRTUALIZATION FOR SMART FACTORIES

Autori: Martins, André Fagundes Castro, Hugo Filipe Costelha de Neves, Carlos Fernando Couceiro de Sousa a ďalší

Prispievatelia: Martins, André Fagundes Castro, Hugo Filipe Costelha de Neves, Carlos Fernando Couceiro de Sousa a ďalší

Zdroj: urn:tid:202654184

Predmety: Industry 4.0, Virtualization, Digital Twin, Virtual Commissioning, Machine Monitoring, OPC UA

Popis súboru: application/pdf

Dostupnosť: http://hdl.handle.net/10400.8/5407

Developing an OPC UA Server for CNC Machines

Autori: Martins, André Lucas, João Costelha, Hugo a ďalší

Prispievatelia: Martins, André Lucas, João Costelha, Hugo a ďalší

Predmety: Industry 4.0, OPC UA, Machine Monitoring, Computer Numeric Control

Popis súboru: application/pdf

Relation: 18770509

Dostupnosť: http://hdl.handle.net/10400.8/8885

A fundamental approach for data acquisition on machine tools as enabler for analytical Industrie 4.0 applications

Autori: Gittler, Thomas Gontarz, Adam Weiss, Lukas a ďalší

Prispievatelia: Gittler, Thomas Gontarz, Adam Weiss, Lukas a ďalší

Zdroj: Procedia CIRP, 79
12th CIRP Conference on Intelligent Computation in Manufacturing Engineering

Predmety: Machine tools, 0209 industrial biotechnology, statistical analysis, 0203 mechanical engineering, data acquisition, ICT, data analysis, Machine Monitoring, Efficiency improvements, Industrie 4.0, 02 engineering and technology

Popis súboru: application/application/pdf

Prístupová URL adresa: https://www.research-collection.ethz.ch/handle/20.500.11850/338956
https://www.sciencedirect.com/science/article/pii/S2212827119302057
http://hdl.handle.net/20.500.11850/338956

Digital twin design for real-time monitoring – a case study of die cutting machine

Autori: Wang, K.-J. Lee, Y.-H. Angelica, S.

Zdroj: International Journal of Production Research. 59:6471-6485

Predmety: 0209 industrial biotechnology, Dashboard system, 9. Industry and infrastructure, digital twin, smart manufacturing, 02 engineering and technology, machine monitoring, 7. Clean energy

Popis súboru: 111 bytes; text/html

Prístupová URL adresa: https://www.tandfonline.com/doi/full/10.1080/00207543.2020.1817999

Cyber-Physical Systems technologies as a key factor in the process of Industry 4.0 and Smart Manufacturing development

Autori: Zubrzycki Jaroslaw() Świć Antoni() Sobaszek, Łukasz(https://viaf.org/viaf/46156009959149581827) a ďalší

Predmety: Industry 4.0, CPS, IoT, Machine monitoring

Popis súboru: application/pdf

Relation: info:eu-repo/grantAgreement/EC/FP7/246686; http://purl.tuc.gr/dl/dias/242F3A46-2B9E-4774-8172-2CA4664552F9

Dostupnosť: http://purl.tuc.gr/dl/dias/242F3A46-2B9E-4774-8172-2CA4664552F9

Vibration Analysis in Industry 4.0: Machine Learning, Energy Harvesting, and Bibliometric Analysis

Autori: Arshad Mehmood

Zdroj: Journal of Science & Technology ; Vol. 8 No. 5 (2023): Volume 8 Issue 5; 33 - 47 ; 2456-5660

Predmety: Linear Vibration, Industry 4.0, nonlinear vibration, Vibration analysis for machine monitoring and diagnosis, Energy harvesting

Popis súboru: application/pdf

Relation: https://jst.org.in/index.php/pub/article/view/773/702; https://jst.org.in/index.php/pub/article/view/773

Dostupnosť: https://jst.org.in/index.php/pub/article/view/773
https://doi.org/10.46243/jst.2023.v8.i05.pp33-47

CNC Machines Integration in Smart Factories using OPC UA

Autori: Martins, André Lucas, João Costelha, Hugo a ďalší

Prispievatelia: Martins, André Lucas, João Costelha, Hugo a ďalší

Zdroj: Journal of Industrial Information Integration

Predmety: Equipment integration, Smart factory, Industry 4.0, OPC UA, Machine monitoring, Computer numeric control, Department of Electrical & Electronic Engineering

Popis súboru: application/pdf

Relation: https://research.thea.ie/handle/20.500.12065/4563; 11; 34

Dostupnosť: https://research.thea.ie/handle/20.500.12065/4563
https://hdl.handle.net/20.500.12065/4563
https://doi.org/10.1016/j.jii.2023.100482

Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework

Autori: Tnani, Mohamed-Ali Subarnaduti, Paul Diepold, Klaus

Predmety: info:eu-repo/classification/ddc, Article, feature learning, CNC machining, machine monitoring, machine learning, few-shot learning, vibration data, two-stage learning

Popis súboru: application/pdf

Relation: https://mediatum.ub.tum.de/1685291; https://mediatum.ub.tum.de/doc/1685291/document.pdf

Dostupnosť: https://mediatum.ub.tum.de/1685291
https://mediatum.ub.tum.de/doc/1685291/document.pdf
https://doi.org/10.3390/s22134813