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. 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.
    2. 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.
    3. 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.
    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. Cui, Ye & E, Hanyu & Pedrycz, Witold & Fayek, Aminah Robinson, 2022. "A granular multicriteria group decision making for renewable energy planning problems," Renewable Energy, Elsevier, vol. 199(C), pages 1047-1059.
    3. 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).
    4. 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.
    5. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    6. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    7. Jie Yang & Shaowen Lu & Liangyong Wang, 2020. "Fused magnesia manufacturing process: a survey," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 327-350, February.
    8. 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.
    9. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    10. Rediske, Graciele & Michels, Leandro & Siluk, Julio Cezar Mairesse & Rigo, Paula Donaduzzi & Rosa, Carmen Brum & Lima, Andrei Cunha, 2024. "A proposed set of indicators for evaluating the performance of the operation and maintenance of photovoltaic plants," Applied Energy, Elsevier, vol. 354(PA).
    11. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    12. 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.
    13. Tomasz Tietze & Piotr Szulc & Daniel Smykowski & Andrzej Sitka & Romuald Redzicki, 2021. "Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations," Energies, MDPI, vol. 14(12), pages 1-17, June.
    14. Hua-Xi Zhou & Chang-Guang Zhou & Hu-Tian Feng, 2023. "An integrated lifetime prediction method for double-nut ball screws subject to preload loss failure mode," Journal of Risk and Reliability, , vol. 237(6), pages 1248-1258, December.
    15. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    16. Szoplik, Jolanta & Muchel, Paulina, 2023. "Using an artificial neural network model for natural gas compositions forecasting," Energy, Elsevier, vol. 263(PD).
    17. Dongkyu Lee & Jinhwa Jeong & Sung Hoon Yoon & Young Tae Chae, 2019. "Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network," Energies, MDPI, vol. 12(17), pages 1-17, August.
    18. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    19. Ioannis Mallidis & Volha Yakavenka & Anastasios Konstantinidis & Nikolaos Sariannidis, 2021. "A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    20. 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.

    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.