IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1653-d115704.html
   My bibliography  Save this article

Lean Maintenance Applied to Improve Maintenance Efficiency in Thermoelectric Power Plants

Author

Listed:
  • Orlando Duran

    (Mechanical Engineering School, Pontificia Universidad Católica de Valparaíso, Valparaiso 2430000, Chile)

  • Andrea Capaldo

    (Department of Mechanical Engineering, Politécnico de Milán, Politecnico de Milán, 20133 Milano MI, Italy)

  • Paulo Andrés Duran Acevedo

    (Inacap Valparaiso, Universidad Tecnológica de Chile INACAP, Valparaiso 2430000, Chile)

Abstract

Thermoelectric power plants consist of a set of critical equipment that require high levels of availability and reliability. Due to this, maintenance of these physical assets is gaining momentum in industry. Maintenance is considered as an activity that contributes to improving the availability, efficiency and productivity of each piece of equipment. Several techniques have been used to achieve greater efficiencies in maintenance, among which we can find the lean maintenance philosophy. Despite the wide diffusion of lean maintenance, there is no structured method that supports the prescription of lean tools applied to the maintenance function. This paper presents the experience gathered in two lean maintenance projects in thermoelectric power plants. The application of lean techniques was based on using a previously developed multicriterial decision making process that uses the Fuzzy Analytic Hierarchy Process (AHP) methodology to carry out a diagnosis and prescription tasks. That methodology allowed the prescription of the appropriated lean techniques to resolve the main deficiencies in maintenance function. The results of applying such lean tools show that important results can be obtained, making the maintenance function in thermoelectric power plants more efficient and lean.

Suggested Citation

  • Orlando Duran & Andrea Capaldo & Paulo Andrés Duran Acevedo, 2017. "Lean Maintenance Applied to Improve Maintenance Efficiency in Thermoelectric Power Plants," Energies, MDPI, vol. 10(10), pages 1-21, October.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1653-:d:115704
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1653/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/10/1653/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. Anand & Rambabu Kodali, 2009. "Development of a framework for lean manufacturing systems," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 5(5), pages 687-716.
    2. Alberto Pliego Marugán & Fausto Pedro García Márquez & Jesús María Pinar Pérez, 2016. "Optimal Maintenance Management of Offshore Wind Farms," Energies, MDPI, vol. 9(1), pages 1-20, January.
    3. Milton Fonseca Junior & Ubiratan Holanda Bezerra & Jandecy Cabral Leite & Jorge Laureano Moya Rodríguez, 2017. "Maintenance Tools applied to Electric Generators to Improve Energy Efficiency and Power Quality of Thermoelectric Power Plants," Energies, MDPI, vol. 10(8), pages 1-21, July.
    4. Peyman Mazidi & Yaser Tohidi & Miguel A. Sanz-Bobi, 2017. "Strategic Maintenance Scheduling of an Offshore Wind Farm in a Deregulated Power System," Energies, MDPI, vol. 10(3), pages 1-20, March.
    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. Ravi Anant Kishore & Roop L. Mahajan & Shashank Priya, 2018. "Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator," Energies, MDPI, vol. 11(9), pages 1-17, August.

    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. Liuming Jing & Dae-Hee Son & Sang-Hee Kang & Soon-Ryul Nam, 2017. "Unsynchronized Phasor-Based Protection Method for Single Line-to-Ground Faults in an Ungrounded Offshore Wind Farm with Fully-Rated Converters-Based Wind Turbines," Energies, MDPI, vol. 10(4), pages 1-15, April.
    2. Ravindra Ojha, 2021. "Enablers of Lean for Manufacturing Excellence: An Interpretive Structural Modelling and Analysis," Vision, , vol. 26(1), pages 90-104, March.
    3. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    4. Paweł Ziemba & Jarosław Wątróbski & Magdalena Zioło & Artur Karczmarczyk, 2017. "Using the PROSA Method in Offshore Wind Farm Location Problems," Energies, MDPI, vol. 10(11), pages 1-20, November.
    5. Ziemba, Paweł, 2022. "Uncertain Multi-Criteria analysis of offshore wind farms projects investments – Case study of the Polish Economic Zone of the Baltic Sea," Applied Energy, Elsevier, vol. 309(C).
    6. Ade Irawan, Chandra & Starita, Stefano & Chan, Hing Kai & Eskandarpour, Majid & Reihaneh, Mohammad, 2023. "Routing in offshore wind farms: A multi-period location and maintenance problem with joint use of a service operation vessel and a safe transfer boat," European Journal of Operational Research, Elsevier, vol. 307(1), pages 328-350.
    7. Walgern, Julia & Peters, Lennart & Madlener, Reinhard, 2017. "Economic Evaluation of Maintenance Strategies for Offshore Wind Turbines Based on Condition Monitoring Systems," FCN Working Papers 8/2017, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    8. Cevasco, D. & Koukoura, S. & Kolios, A.J., 2021. "Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    9. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    10. Sidney Jose Meireles de Andrade & Plácido Rogério Pinheiro & Glauber Jean Alves Narciso & José Tarcisio Pimentel Neto & João Pedro da Silva Bandeira & Vinicius Sales de Andrade & Cayo Cid de França Mo, 2022. "Prioritising Maintenance Work Orders in a Thermal Power Plant: A Multicriteria Model Application," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    11. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    12. Zbigniew Nadolny, 2022. "Determination of Dielectric Losses in a Power Transformer," Energies, MDPI, vol. 15(3), pages 1-14, January.
    13. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    14. Pliego Marugán, Alberto & García Márquez, Fausto Pedro & Pinar Pérez, Jesús María, 2022. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. Arcos Jiménez, Alfredo & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 2-12.
    16. Min-Jun Kim & Sang-Hwan Bak & Woo-Chul Jung & Deog-Jae Hur & Dong-Shin Ko & Man-Sik Kong, 2019. "Improvement of Heat Dissipation Characteristics of Cu Bus-Bar in the Switchboards through Shape Modification and Surface Treatment," Energies, MDPI, vol. 12(1), pages 1-11, January.
    17. Ralf Stetter, 2020. "Approaches for Modelling the Physical Behavior of Technical Systems on the Example of Wind Turbines," Energies, MDPI, vol. 13(8), pages 1-27, April.
    18. Song Lv & Zuoqin Qian & Dengyun Hu & Xiaoyuan Li & Wei He, 2020. "A Comprehensive Review of Strategies and Approaches for Enhancing the Performance of Thermoelectric Module," Energies, MDPI, vol. 13(12), pages 1-24, June.
    19. Luciano C. Siebert & Adriana Sbicca & Alexandre Rasi Aoki & Germano Lambert-Torres, 2017. "A Behavioral Economics Approach to Residential Electricity Consumption," Energies, MDPI, vol. 10(6), pages 1-18, June.
    20. Zhou, Yifan & Miao, Jindan & Yan, Bin & Zhang, Zhisheng, 2020. "Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms," Renewable Energy, Elsevier, vol. 160(C), pages 1136-1147.

    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:10:y:2017:i:10:p:1653-:d:115704. 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.