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Cited by 49Reliability and Maintenance OptimizationDigital Transformation in IndustryMachine Fault Diagnosis Techniques

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Enabling condition-based maintenance decisions with proactive event-driven computing

Bousdekis, Alexandros, Papageorgiou, Nikos, Magoutas, Babis, Apostolou, Dimitris, Mentzas, Gregoris·2018·Computers in Industry
DOI 10.1016/j.compind.2018.04.019
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Crossref · 10.1016/j.compind.2018.04.019

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Citations

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Research works printed but not linked: 23

  1. Karim, 2009, E-maintenance and information logistics: aspects of content format, J. Qual. Maint. Eng., 15, 308, 10.1108/13552510910983242 · DOI
  2. Guillén, 2016, A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies, Comput. Ind., 82, 170, 10.1016/j.compind.2016.07.003 · DOI
  3. Vogl, 2016, A review of diagnostic and prognostic capabilities and best practices for manufacturing, J. Intell. Manuf., 1
  4. Elwany, 2008, Sensor-driven prognostic models for equipment replacement and spare parts inventory, IIE Trans., 40, 629, 10.1080/07408170701730818 · DOI
  5. Feldman, 2013, Proactive event processing in action: a case study on the proactive management of transport processes, Proceedings of the Seventh ACM International Conference on Distributed Event-Based Systems (DEBS’13), 97, 10.1145/2488222.2488274 · DOI
  6. Bousdekis, 2015, A proactive decision making framework for condition-based maintenance, Ind. Manage. Data Syst., 115, 1225, 10.1108/IMDS-03-2015-0071 · DOI
  7. Ahmad, 2012, An overview of time-based and condition-based maintenance in industrial application, Comput. Ind. Eng., 63, 135, 10.1016/j.cie.2012.02.002 · DOI
  8. Desforges, 2017, A prognostic function for complex systems to support production and maintenance co-operative planning based on an extension of object oriented Bayesian networks, Comput. Ind., 86, 34, 10.1016/j.compind.2017.01.002 · DOI
  9. Dong, 2017, Application of internet of things technology on predictive maintenance system of coal equipment, Proc. Eng., 174, 885, 10.1016/j.proeng.2017.01.237 · DOI
  10. Lin, 2017, Development of advanced manufacturing cloud of things (AMCoT)—a smart manufacturing platform, IEEE Robot. Autom. Lett., 2, 1809, 10.1109/LRA.2017.2706859 · DOI
  11. Civerchia, 2017, Industrial internet of things monitoring solution for advanced predictive maintenance applications, J. Ind. Inf. Integr., 7, 4
  12. Yan, 2017, Industrial big data in an industry 4.0 environment: challenges, schemes: and applications for predictive maintenance, IEEE Access, 5, 23484, 10.1109/ACCESS.2017.2765544 · DOI
  13. Li, 2017, Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: industry 4.0 scenario, Adv. Manuf., 5, 377, 10.1007/s40436-017-0203-8 · DOI
  14. Campos, 2009, Development in the application of ICT in condition monitoring and maintenance, Comput. Ind., 60, 1, 10.1016/j.compind.2008.09.007 · DOI
  15. Van Horenbeek, 2013, Joint maintenance and inventory optimization systems: a review, Int. J. Prod. Econ., 143, 499, 10.1016/j.ijpe.2012.04.001 · DOI
  16. Aboelmaged, 2015, E-maintenance research: a multifaceted perspective, J. Manuf. Technol. Manage., 26, 606, 10.1108/JMTM-02-2013-0009 · DOI
  17. Bousdekis, 2015, Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance, J. Intell. Manuf., 1
  18. Wu, 2007, A neural network integrated decision support system for condition-based optimal predictive maintenance policy, IEEE Trans. Syst. Man Cyber.-Part A: Syst. Hum., 37, 226, 10.1109/TSMCA.2006.886368 · DOI
  19. Gegenfurtner, 1992, PRAXIS: Brent’s algorithm for function minimization, Behav. Res. Methods Instrum. Comput., 24, 560, 10.3758/BF03203605 · DOI
  20. Vanneste, 1995, An integrated and structured approach to improve maintenance, Eur. J. Oper. Res., 82, 241, 10.1016/0377-2217(94)00261-A · DOI
  21. El Kadiri, 2016, Current trends on ICT technologies for enterprise information systems, Comput. Ind., 79, 14, 10.1016/j.compind.2015.06.008 · DOI
  22. Wang, 2015, A prognostics-based spare part ordering and system replacement policy for a deteriorating system subjected to a random lead time, Int. J. Prod. Res., 53, 4511, 10.1080/00207543.2014.988892 · DOI
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Cited by

  • Prescriptive analytics: Literature review and research challenges

    Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.003 · Cited by 501

    “…making problems in different contexts are: Artificial Neural Networks (Domingos, Burguillo, & Lenaerts, 2017), Bayesian Networks (Said, Shahzad, Zamai, Hubac, & Tollenaere, 2016), topic models (Roberts et al., 2014), fuzzy sets (Pal & Ceglarek, 2013), Markov models (Bousdekis et al., 2018).”
    Self-citation

Metadata

Authors
Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G.
Affiliations
  • National Technical University of Athens (GR)
  • University of Piraeus (GR)
Corresponding author
Bousdekis, Alexandros
ORCID
Venue
Computers in Industry
Publisher
Elsevier BV
Published
2018-09
Volume
100
Pages
173-183
ISSN
0166-3615 (ISSN-L)
DOI
10.1016/j.compind.2018.04.019
OpenAlex
W2802725548
Cited by
49
References
2
Citations by year
2026: 5 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 15 · 2021: 5 · 2020: 9 · 2019: 7 · 2018: 1
FWCI
6.66
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
Computers in Industry (publishedVersion)
Funders
European Commission
Topics
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
Keywords
condition-based maintenance, maintenance decision-making, Internet of Things, proactive maintenance, oil and gas industry

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.