IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i15p3762-d387888.html
   My bibliography  Save this article

Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models

Author

Listed:
  • Adolfo Crespo Marquez

    (Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain)

  • Juan Francisco Gomez Fernandez

    (Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain)

  • Pablo Martínez-Galán Fernández

    (Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain)

  • Antonio Guillen Lopez

    (Department of Industrial Management, School of Engineering, University of Seville, 41092 Seville, Spain)

Abstract

Maintenance Management is a key pillar in companies, especially energy utilities, which have high investments in assets, and so for its proper contribution has to be integrated and aligned with other departments in order to conserve the asset value and guarantee the services. In this line, Intelligent Assets Management Platforms (IAMP) are defined as software platforms to collect and analyze data from industrial assets. They are based on the use of digital technologies in industry. Beside the fact that monitoring and managing assets over the internet is gaining ground, this paper states that the IAMPs should also support a much better balanced and more strategic view in existing asset management and concretely in maintenance management. The real transformation can be achieved if these platforms help to understand business priorities in work and investments. In this paper, we first discuss about the factors explaining IAMP growth, then we explain the importance of considering, well in advance, those managerial aspects of the problem, for proper investments and suitable digital transformation through the adoption and use of IAMPs. A case study in the energy sector is presented to map, or to identify, those platform modules and Apps providing important value-added features to existing asset management practices. Later, attention is paid to the methodology used to develop the Apps’ data models from a maintenance point of view. To illustrate this point, a methodology for the development of the asset criticality analysis process data model is proposed. Finally, the paper includes conclusions of the work and relevant literature to this research.

Suggested Citation

  • Adolfo Crespo Marquez & Juan Francisco Gomez Fernandez & Pablo Martínez-Galán Fernández & Antonio Guillen Lopez, 2020. "Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models," Energies, MDPI, vol. 13(15), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3762-:d:387888
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/15/3762/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/15/3762/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    2. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
    3. Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fausto Pedro García Márquez, 2022. "Special Issue on Advances in Maintenance Management," Energies, MDPI, vol. 15(7), pages 1-4, March.
    2. Saihi, Afef & Ben-Daya, Mohamed & As'ad, Rami, 2023. "Underpinning success factors of maintenance digital transformation: A hybrid reactive Delphi approach," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Martínez-Galán Fernández, Pablo & Guillén López, Antonio J. & Márquez, Adolfo Crespo & Gomez Fernández, Juan Fco. & Marcos, Jose Antonio, 2022. "Dynamic Risk Assessment for CBM-based adaptation of maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Sandra Giraldo & David la Rotta & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortúa, 2021. "Digital Transformation of Energy Companies: A Colombian Case Study," Energies, MDPI, vol. 14(9), pages 1-14, April.
    5. Damjan Maletič & Matjaž Maletič & Basim Al-Najjar & Boštjan Gomišček, 2020. "An Analysis of Physical Asset Management Core Practices and Their Influence on Operational Performance," Sustainability, MDPI, vol. 12(21), pages 1-20, October.
    6. Fco. Javier García-Gómez & Víctor Fco. Rosales-Prieto & Alberto Sánchez-Lite & José Luis Fuentes-Bargues & Cristina González-Gaya, 2021. "An Approach to Sustainability Risk Assessment in Industrial Assets," Sustainability, MDPI, vol. 13(12), pages 1-23, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    2. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    3. Mohsen Beigi & Hossein Beigi Harchegani & Mehdi Torki & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Jacek Dziwulski, 2022. "Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches," Sustainability, MDPI, vol. 14(5), pages 1-12, March.
    4. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
    5. Maolin Cheng & Jiano Li & Yun Liu & Bin Liu, 2020. "Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    6. Nikolaos Kolokas & Dimosthenis Ioannidis & Dimitrios Tzovaras, 2021. "Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization," Energies, MDPI, vol. 14(11), pages 1-36, May.
    7. Riku-Pekka Nikula & Konsta Karioja & Kauko Leiviskä & Esko Juuso, 2019. "Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1563-1579, April.
    8. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    9. Rita Teixeira & Adelaide Cerveira & Eduardo J. Solteiro Pires & José Baptista, 2024. "Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods," Energies, MDPI, vol. 17(14), pages 1-30, July.
    10. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    11. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
    12. Thi-Tinh Le & Seok-Ju Lee & Minh-Chau Dinh & Minwon Park, 2023. "Design of an Improved Remaining Useful Life Prediction Model Based on Vibration Signals of Wind Turbine Rotating Components," Energies, MDPI, vol. 17(1), pages 1-18, December.
    13. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
    14. Jonghoon Ahn, 2022. "A Network-Based Strategy to Increase the Sustainability of Building Supply Air Systems Responding to Unexpected Temperature Patterns," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
    15. Nkounga, Willy Magloire & Ndiaye, Mouhamadou Falilou & Cisse, Oumar & Grandvaux, Françoise & Tabourot, Laurent & Ndiaye, Mamadou Lamine, 2022. "Automatic control and dispatching of charging currents to a charging station for power-assisted bikes," Energy, Elsevier, vol. 246(C).
    16. Ke Zhou & Ziji Zhao & Lin Xia & Jinghua Wu, 2024. "Advancing Grey Modeling with a Novel Time-Varying Approach for Predicting Solar Energy Generation in the United States," Sustainability, MDPI, vol. 16(24), pages 1-17, December.
    17. Zhe Li & Yi Wang & Kesheng Wang, 2020. "A data-driven method based on deep belief networks for backlash error prediction in machining centers," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1693-1705, October.
    18. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    19. Aldemar Leguizamon-Perilla & Juan S. Rodriguez-Bernal & Laidi Moralez-Cruz & Nidia Isabel Farfán-Martinez & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortua, 2023. "Digitalisation and Modernisation of Hydropower Operating Facilities to Support the Colombian Energy Mix Flexibility," Energies, MDPI, vol. 16(7), pages 1-17, March.
    20. Lee-Yong Sung & Jonghoon Ahn, 2020. "Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment," Energies, MDPI, vol. 13(5), pages 1-15, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3762-:d:387888. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.