Intelligent predictive maintenance and remote monitoring framework for industrial equipment based on mixed reality
Open accessAbstract
The currently applied maintenance strategies, including Reactive and Preventive maintenance can be considered obsolete. The constant improvements in Information and Communication Technologies as well as in Digital Technologies along with the increase of computational power, have facilitated the development of new Artificial Intelligence algorithms to integrate cognition in computational systems. This trend is posing a great challenge for engineers, as such developments will enable the creation of robust systems that can monitor the current status of the machines and by extension to predict unforeseeable situations. Furthermore, Smart Computers will be capable of examining all possible scenarios and suggest viable solutions in a fraction of time compared to humans. Therefore, in this paper, the modelling, design and development of a Predictive Maintenance and Remote Monitoring system are proposed, based on the utilization of Artificial Intelligence algorithms for data acquisition, fusion, and post-processing. In addition to that, the proposed framework will integrate a Mixed Reality application for the intuitive visualization of the data, that will ultimately facilitate production and maintenance engineers to monitor the condition of the machines, and most importantly to get an accurate prediction of the oncoming failures.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The rapid advancements in BDA, ML, and AI have enabled DTs to reduce maintenance expenses and enhance overall system performance.
“The rapid advancements in BDA, ML, and AI have enabled DTs to reduce maintenance expenses and enhance overall system performance [66,67].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [67]
The State of the Art: Machine Learning section says ML-based predictive maintenance can yield cost savings and increased system availability. Digital Twin Development describes using a digital twin to predict malfunctions and minimize downtime. The paper does not establish the full claim that rapid advances in BDA, ML, and AI have enabled digital twins to reduce maintenance expenses and enhance overall system performance. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634 · see citing works
Data-driven smart manufacturing
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.006 · Cited by 1,934 · see citing works
Remaining useful life estimation in prognostics using deep convolution neural networks
Li, X., Ding, Q., & Sun, J. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.11.021 · Cited by 1,773 · see citing works
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.028 · Cited by 1,162 · see citing works
Manufacturing Systems: Theory and Practice
Chryssolouris, G. (2006) · Springer eBooks · doi:10.1007/0-387-28431-1 · Cited by 912 · see citing works
A systematic review of augmented reality applications in maintenance
Palmarini, R., Erkoyuncu, J. A., Roy, R., & Torabmostaedi, H. (2017) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2017.06.002 · Cited by 783 · see citing works
Simulation in the design and operation of manufacturing systems: state of the art and new trends
Mourtzis, D. (2019) · International Journal of Production Research · doi:10.1080/00207543.2019.1636321 · Cited by 620 · see citing works
Self-citationLi, X., We, Z., & Ding, Q. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.11.011 · Cited by 589 · see citing works
Chen, J., Jing, H., Chang, Y., & Liu, Q. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.01.006 · Cited by 498 · see citing works
Augmented reality in support of intelligent manufacturing – A systematic literature review
Egger, J., & Masood, T. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2019.106195 · Cited by 400 · see citing works
SCADA security in the light of cyber-warfare
Nicholson, A., Webber, S., Dyer, S., Patel, T., & Janicke, H. (2012) · Computers & Security · doi:10.1016/j.cose.2012.02.009 · Cited by 334 · see citing works
A review of diagnostic and prognostic capabilities and best practices for manufacturing
Vogl, G. W., Weiss, B. A., & Helu, M. (2016) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-016-1228-8 · Cited by 325 · see citing works
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
Rezaeianjouybari, B., & Shang, Y. (2020) · Measurement · doi:10.1016/j.measurement.2020.107929 · Cited by 318 · see citing works
Fantini, P., Pinzone, M., & Taisch, M. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.01.025 · Cited by 309 · see citing works
Λούτας, Θ., Roulias, D., & Georgoulas, G. (2013) · IEEE Transactions on Reliability · doi:10.1109/tr.2013.2285318 · Cited by 302 · see citing works
Banerjee, I., Yuan, L., Chen, M. C., Hasan, S. A., Langlotz, C. P., Moradzadeh, N., Chapman, B. E., Amrhein, T. J., Mong, D. A., Rubin, D. L., et al. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.11.004 · Cited by 283 · see citing works
Remaining useful life prediction of aircraft engine based on degradation pattern learning
Zhao, Z., Liang, B., Wang, X., & Lu, W. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.02.007 · Cited by 268 · see citing works
Testing and validating Extended Reality (xR) technologies in manufacturing
Fast–Berglund, Å., Gong, L., & Li, D. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.06.054 · Cited by 245 · see citing works
Supporting Remote Maintenance in Industry 4.0 through Augmented Reality
Masoni, R., Ferrise, F., Bordegoni, M., Gattullo, M., Uva, A. E., Fiorentino, M., Carrabba, E., & Donato, M. D. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.257 · Cited by 238 · see citing works
Real-Time Remote Maintenance Support Based on Augmented Reality (AR)
Mourtzis, D., Siatras, V., & Angelopoulos, J. (2020) · Applied Sciences · doi:10.3390/app10051855 · Cited by 198 · see citing works
Self-citationKong, Z., Cui, Y., Zhou, X., & Lv, H. (2019) · Applied Sciences · doi:10.3390/app9194156 · Cited by 172 · see citing works
Remaining useful life prediction based on health index similarity
Liu, Y., Hu, X., & Zhang, W. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.02.002 · Cited by 167 · see citing works
A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring
Mourtzis, D., Vlachou, E., Milas, N. T., & Xanthopoulos, N. (2016) · Procedia CIRP · doi:10.1016/j.procir.2015.12.069 · Cited by 142 · see citing works
Self-citationMourtzis, D., Vlachou, A., & Zogopoulos, V. (2017) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4035721 · Cited by 130 · see citing works
Self-citationWen, P., Zhao, S., Chen, S., & Li, Y. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107241 · Cited by 125 · see citing works
Pang, Y., He, Q., Jiang, G., & Xie, P. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.06.154 · Cited by 121 · see citing works
Mourtzis, D., Siatras, V., Angelopoulos, J., & Panopoulos, N. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.04.076 · Cited by 92 · see citing works
Self-citationAssessing augmented reality in production: remote-assisted maintenance with HoloLens
Vorraber, W., Gasser, J., Webb, H., Neubacher, D., & Url, P. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.05.025 · Cited by 81 · see citing works
Mourtzis, D., Zogopoulos, V., Katagis, I., & Lagios, P. (2018) · Procedia CIRP · doi:10.1016/j.procir.2018.02.045 · Cited by 74 · see citing works
Self-citationWang, H., Peng, M.-J., Miao, Z., Liu, Y.-K., Ayodeji, A., & Hao, C. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.08.031 · Cited by 74 · see citing works
Remaining useful life prediction of induction motors using nonlinear degradation of health index
Yang, F., Habibullah, M. S., & Shen, Y. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107183 · Cited by 71 · see citing works
SCADA-data-based wind turbine fault detection: A dynamic model sensor method
Zhang, S., & Lang, Z. (2020) · Control Engineering Practice · doi:10.1016/j.conengprac.2020.104546 · Cited by 69 · see citing works
Enhancing cutting tool sustainability based on remaining useful life prediction
Sun, H., Liu, Y., Pan, J., Zhang, J., & Ji, W. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118794 · Cited by 68 · see citing works
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.04.130 · Cited by 39 · see citing works
Self-citationA Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
Yang, H., Zhao, F., Jiang, G., Sun, Z., & Mei, X. (2019) · Applied Sciences · doi:10.3390/app9224813 · Cited by 33 · see citing works
Identifying early defects of wind turbine based on SCADA data and dynamical network marker
Ruiming, F., Minling, W., xinhua, G., Shang, R., & Pengfei, S. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.03.036 · Cited by 31 · see citing works
Wolfartsberger, J., Zenisek, J., & Wild, N. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.04.022 · Cited by 31 · see citing works
Multi-objective adjustment of remaining useful life predictions based on reinforcement learning
Kozjek, D., Malus, A., & Vrabič, R. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.03.051 · Cited by 13 · see citing works
Mitchell, R. (1995) · doi:10.1007/978-1-349-13812-8_7 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Ahmed, M., and Khan, A.-S.P. (2019). Data analytics: concepts, techniques, and applications. Boca Raton, FL: CRC Press Taylor & Francis Group.
Cited by
Sorted by citations
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022) · Energies · doi:10.3390/en15176276 · Cited by 530 · see citing works
Self-citationUçar, A., Karaköse, M., & Kırımça, N. (2024) · Applied Sciences · doi:10.3390/app14020898 · Cited by 453 · see citing works
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2021) · Electronics · doi:10.3390/electronics10243175 · Cited by 140 · see citing works
Self-citationLiu, C., Zhu, H., Tang, D., Nie, Q., Zhou, T., Wang, L., & Song, Y. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102357 · Cited by 133 · see citing works
A predictive maintenance model for optimizing production schedule using deep neural networks
Zonta, T., da Costa, C. A., Zeiser, F. A., Ramos, G. D. O., Kunst, R., & Righi, R. D. R. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.12.013 · Cited by 112 · see citing works
Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure
Mahmoodian, M., Shahrivar, F., Setunge, S., & Mazaheri, S. (2022) · Sustainability · doi:10.3390/su14148664 · Cited by 110 · see citing works
Risks and uncertainties in carbon capture, transport, and storage projects: A comprehensive review
Mahjour, S. K., & Faroughi, S. A. (2023) · Gas Science and Engineering · doi:10.1016/j.jgsce.2023.205117 · Cited by 97 · see citing works
Augmented Reality in Surgery: A Scoping Review
Barcali, E., Iadanza, E., Manetti, L., Francia, P., Nardi, C., & Bocchi, L. (2022) · Applied Sciences · doi:10.3390/app12146890 · Cited by 92 · see citing works
Stavropoulos, P., & Mourtzis, D. (2022) · Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology · doi:10.1016/b978-0-12-823657-4.00010-5 · Cited by 80 · see citing works
Self-citationMourtzis, D., Angelopoulos, J., & Panopoulos, N. (2021) · Procedia Manufacturing · doi:10.1016/j.promfg.2021.07.025 · Cited by 64 · see citing works
Self-citationA Predictive Maintenance Model for Flexible Manufacturing in the Context of Industry 4.0
Sang, G. M., Xu, L., & de Vrieze, P. T. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.663466 · Cited by 62 · see citing works
A Smart IoT Platform for Oncology Patient Diagnosis based on AI: Towards the Human Digital Twin
Mourtzis, D., Angelopoulos, J., Panopoulos, N., & Kardamakis, D. M. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.11.284 · Cited by 58 · see citing works
Self-citationDigital twin inception in the Era of industrial metaverse
Mourtzis, D. (2023) · Frontiers in Manufacturing Technology · doi:10.3389/fmtec.2023.1155735 · Cited by 56 · see citing works
Self-citationSamadhiya, A., Agrawal, R., Luthra, S., Anil Kumar, K., Garza-Reyes, J., & Srivastava, D. (2022) · International Journal of Logistics Management · doi:10.1108/ijlm-04-2022-0192 · Cited by 44 · see citing works
Angelopoulos, J., & Mourtzis, D. (2022) · Applied Sciences · doi:10.3390/app12115349 · Cited by 48 · see citing works
Self-citationThe impact of Industry 4.0 on the relationship between TPM and maintenance performance
Tortorella, G. L., Saurin, T. A., Fogliatto, F. S., Tlapa, D., Moyano‐Fuentes, J., Gaiardelli, P., Seyedghorban, Z., Vassolo, R. S., Cawley, A. F. M., Vijaya Sunder, M., et al. (2022) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-10-2021-0399 · Cited by 43 · see citing works
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.259 · Cited by 43 · see citing works
Self-citationReal Estate App Development Based on AI/VR Technologies
Miljković, I., Shlyakhetko, O., & Федушко, С. (2023) · Electronics · doi:10.3390/electronics12030707 · Cited by 41 · see citing works
A survey on HoloLens AR in support of human-centric intelligent manufacturing
Fang, W., Zhang, T., Chen, L., & Hu, H. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02247-5 · Cited by 34 · see citing works
Eswaran, M., Samuel, P. J. C., Karthika, S. K., Yuvaraju, B. A. G., Inkulu, A. K., & Bahubalendruni, M. V. A. R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130877 · Cited by 32 · see citing works
Gasiyarov, V., Radionov, A., Loginov, B., Karandaev, A., Gasiyarova, O., & Khramshin, V. (2023) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp7010041 · Cited by 27 · see citing works
Review of machine learning technologies and artificial intelligence in modern manufacturing systems
Nassehi, A., Zhong, R. Y., Li, X., & Epureanu, B. I. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823657-4.00002-6 · Cited by 27 · see citing works
AI-Driven Decision-Making Applications in Pharmaceutical Sciences
Yingngam, B., Navabhatra, A., & Sillapapibool, P. (2024) · Advances in media, entertainment and the arts (AMEA) book series · doi:10.4018/979-8-3693-0639-0.ch001 · Cited by 26 · see citing works
Kapoor, D., Gupta, D., & Uppal, M. (2025) · Discover Computing · doi:10.1007/s10791-025-09653-8 · Cited by 23 · see citing works
Olalere, I. O., & Olanrewaju, O. A. (2023) · Applied Sciences · doi:10.3390/app13042248 · Cited by 23 · see citing works
Industry 4.0 and smart manufacturing
Mourtzis, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13924-6.00002-8 · Cited by 23 · see citing works
Self-citationDigital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26 · see citing works
Machine Vision Based Predictive Maintenance for Machine Health Monitoring: A Comparative Analysis
Haq, I. U., Anwar, S., & Khan, T. (2023) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai57502.2023.10089572 · Cited by 13 · see citing works
Mobile Diminished Reality for Preserving 3D Visual Privacy
Tabet, S., Kayssi, A. I., & Elhajj, I. H. (2023) · International Conference on Intelligent Metaverse Technologies & Applications (iMETA) · doi:10.1109/imeta59369.2023.10294436 · Cited by 10 · see citing works
Bibliometric and content analyses of research on technology use in real estate facilities management
Okoro, C. S. (2023) · Facilities · doi:10.1108/f-10-2022-0128 · Cited by 6 · see citing works
Comparison between data maturity and maintenance strategy: A case sutdy
Brasen, L. P. H., Grooss, O. F., & Tambo, T. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.11.324 · Cited by 6 · see citing works
Esengün, M., Üstündağ, A., & İnce, G. (2022) · IISE Transactions · doi:10.1080/24725854.2022.2034195 · Cited by 6 · see citing works
Slags as Thermal Energy Storage Media for Concentrated Solar Power and Renewable Energy Integration
Enemuo, M., Enemuo, N., Taleghani, A. D., & Ogunmodimu, O. S. O. (2025) · Energy Storage · doi:10.1002/est2.70274 · Cited by 5 · see citing works
Augmented Reality and Indoor Positioning in Context of Smart Industry: A Review
Chandel, K., Åhlén, J., & Seipel, S. (2022) · Management and Production Engineering Review · doi:10.24425/mper.2022.142396 · Cited by 5 · see citing works
Overview of Maintenance Management Strategies in the Industry
Adaramola, B., Kayode, J. F., Monye, S. I., & Afolalu, S. A. (2024) · International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) · doi:10.1109/seb4sdg60871.2024.10629938 · Cited by 5 · see citing works
Andrioaia, D. A. (2025) · Sensors · doi:10.3390/s25154782 · Cited by 4 · see citing works
A mixed reality-based remote collaboration framework using improved pose estimation
Oh, I., Jang, G., Song, J., Son, M., Kim, D., Yun, J., & Ko, K. H. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104414 · Cited by 3 · see citing works
Enhancing failure analysis with augmented reality: insights from a rolling stock case study
Scheffer, S., Martinetti, A., Damgrave, R., & van Dongen, L. A. M. (2025) · Production & Manufacturing Research · doi:10.1080/21693277.2025.2595446 · Cited by 3 · see citing works
Challenges and opportunities in warehouse management in the context of Industry 5.0
Kumar, R. J., Kumar, I. A., Ronald Aseer, J., & Bahubalendruni, M. V. A. R. (2026) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2026.101120 · Cited by 3 · see citing works
Predictive maintenance: assessment of potentials for residential heating systems
Arora, S.-J., & Rabe, M. (2023) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2023.2204471 · Cited by 3 · see citing works
Adaptive, predictive machine condition assessment for resilient digital solutions
Pfaff-Kastner, M. M.-L., Doerrer, F., Frieß, U., Praedicow, M., & Pütz, M. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.11.138 · Cited by 3 · see citing works
Digital Twinning Stacks, a modular communication system approach for IOT implementation in Unity3D
Rieder, J. S. I., Stephan, L., & Aschenbrenner, D. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.10.165 · Cited by 2 · see citing works
Digital Models, Sensor Network and Autonomous Telemetry Modules for Cryogenic Storage Systems
Soldatov, E. S., & Bogomolov, A. V. (2024) · Journal of Computational and Engineering Mathematics · doi:10.14529/jcem240105 · Cited by 2 · see citing works
Xia, J., Jin, T., & Fan, L. (2022) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1109/icsps58776.2022.00149 · Cited by 1 · see citing works
Digital Twins for Bridge Assessment and Maintenance
Khoshraftar, A. S., van der Zwaag, B. J., Le, D. V., & İncel, Ö. D. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106891 · Cited by 1 · see citing works
Albattat, S. M., Albaker, B. M., & Alsaedi, M. A. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.12455 · Cited by 1 · see citing works
ABEDALRHMAN, K., & Alzaydi, A. A. (2026) · Applied Science and Biotechnology Journal for Advanced Research · doi:10.31033/abjar/5.3.2026.117 · Cited by 1 · see citing works
Predictive maintenance on turbo fan dataset using deep learning
Ghadekar, P. P., Dargode, A., Deshmukh, J., Jaisinghani, A., Jadhav, M., & Nimbalkar, A. G. (2023) · AIP conference proceedings · doi:10.1063/5.0182698 · Cited by 1 · see citing works
Compatibility of Blockchains in the Internet of Vehicles
Sanni, C. A., & Chaudhry, U. B. (2025) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-96782-5_8 · Cited by 1 · see citing works
Arora, S.-J., Ebbecke, C., Rabe, M., & Fisch, J. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.11.119 · Cited by 1 · see citing works
Research on rapier loom fault system based on cloud-side collaboration
Xiao, Y., Wang, K., Liu, W., Peng, K., & Wan, F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260888 · Cited by 1 · see citing works
Digital Twin for Predictive Maintenance of Palm Oil Processing Machines
Marchiningrum, A. U., & Albarda, I. (2022) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee57756.2022.10057848 · Cited by 1 · see citing works
A Conceptual Framework for the Improvement of Robotic System Reliability Through Industry 4.0
Mourtzis, D., Tsoubou, S., & Angelopoulos, J. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-38241-3_8 · Cited by 1 · see citing works
Self-citationMantenimiento en la Industria 4.0 basado en realidad aumentada
Miguel-Pérez, A. D., Marquez-Romero, P., & Márquez-Sevillano, J. (2022) · Congreso Iberoamericano de Ingeniería Mecánica (CIBIM) · doi:10.5944/bicim2022.353
Солдатов, Е. С. (2023) · Экономика Информатика · doi:10.52575/2712-746x-2023-50-4-893-900
Информационная поддержка принятия решений при мониторинге состояния криогенного оборудования
Солдатов, Е. С., & Evgeny, S. (2024) · Международный журнал Программные продукты и системы · doi:10.15827/0236-235x.145.077-082
Soldatov, E. S. (2023) · Journal of Computational and Engineering Mathematics · doi:10.14529/jcem230405
Saçaklıdır, İ., & Koç, S. (2025) · Balkan Journal of Electrical and Computer Engineering · doi:10.17694/bajece.1500321
Industrial Metaverse Towards Human-Centric Smart Manufacturing
Mourtzis, D. (2025) · Springer eBooks · doi:10.1007/978-3-031-82170-7_1
Self-citationIntegration of IoT and Machine Learning for Predictive Maintenance in Manufacturing Industries
Singh, P., & Abhishek (2025) · International Journal of Research and Review in Applied Science Humanities and Technology · doi:10.71143/sdwjfp14
Fernunterstützung mit Mixed Reality
Bamming, K., Lückerath, M., Wehrbein, R., Meister, S., Söbke, H., & Wiggenbrock, J. (2025) · Springer eBooks · doi:10.1007/978-3-662-72004-2_16
A Sequential Roadmap of Industry 5.0: Exploring Challenges and Opportunities
joshi, A., Pandey, D., Bisht, D., Kumar, R., & Bhandari, K. (2025) · International Conference on Electronic Circuits and Signaling Technologies (ICECST) · doi:10.1109/icecst66106.2025.11307487
Noor, A., & Kalra, S. (2026) · Springer eBooks · doi:10.1007/978-981-96-8867-8_55-1
Predictive Maintenance – An Overview
Juganaru, M. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1015854
Augmented reality remote maintenance system based on fog computing and 5G networks
Yang, H.-Y., & Chen, S.-L. (2026) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/09544054261458706
Metaverse-Enabled Digital Twin Framework for Predictive Maintenance
Bocanet, V. I., Kostelić, K., Panc, N. A., Gerzic, H., Muntean, M. H., & Fleșeriu, C. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-38606-9_15
Covrig, T., Duka, A.-V., & Miclea, L. C. (2026) · Sensors · doi:10.3390/s26196208
Eswaran, M., Kumar, R. J., Inkulu, A. K., Pradeep, N., & Bahubalendruni, M. V. A. R. (2026) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2026.101222
Mouleshvar, B., Raja, L., Pradeep, N., & Eswaran, M. (2026) · Computers & Industrial Engineering · doi:10.1016/j.cie.2026.112458
- Authors
- Mourtzis, D., Angelopoulos, J., & Panopoulos, N.
- Affiliations
- University of Patras (GR)
- Corresponding author
- Mourtzis, Dimitris
- Venue
- Frontiers in Mechanical Engineering
- Publisher
- Frontiers Media SA
- Published
- 2020-12-17
- Volume
- 6
- ISSN
- 2297-3079 (ISSN-L)
- OpenAlex
- W3111508897
- Cited by
- 70
- References
- 39
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 7 · 2023: 15 · 2022: 16 · 2021: 8
- FWCI
- 4.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Mechanical Engineering (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Horizon 2020 Framework Programme
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- predictive maintenance, remote monitoring, mixed reality, industrial machinery, machine condition monitoring, failure prediction, artificial intelligence, data fusion, data visualization, smart manufacturing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
