IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i22p6202-d284088.html
   My bibliography  Save this article

A Review of Information Fusion Methods for Gas Turbine Diagnostics

Author

Listed:
  • Valentina Zaccaria

    (School of Business, Society and Engineering, Mälardalen University, 721 23 Vasteras, Sweden)

  • Moksadur Rahman

    (School of Business, Society and Engineering, Mälardalen University, 721 23 Vasteras, Sweden)

  • Ioanna Aslanidou

    (School of Business, Society and Engineering, Mälardalen University, 721 23 Vasteras, Sweden)

  • Konstantinos Kyprianidis

    (School of Business, Society and Engineering, Mälardalen University, 721 23 Vasteras, Sweden)

Abstract

The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.

Suggested Citation

  • Valentina Zaccaria & Moksadur Rahman & Ioanna Aslanidou & Konstantinos Kyprianidis, 2019. "A Review of Information Fusion Methods for Gas Turbine Diagnostics," Sustainability, MDPI, vol. 11(22), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6202-:d:284088
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/22/6202/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/22/6202/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zahra Fallahi & Gregor P. Henze, 2019. "Interactive Buildings: A Review," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    2. Feng Lu & Yafan Wang & Jinquan Huang & Yihuan Huang, 2015. "Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter," Energies, MDPI, vol. 8(12), pages 1-17, December.
    3. Feng Lu & Chunyu Jiang & Jinquan Huang & Yafan Wang & Chengxin You, 2016. "A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis," Energies, MDPI, vol. 9(10), pages 1-22, October.
    4. Makbul A.M. Ramli & H.R.E.H. Bouchekara & Abdulsalam S. Alghamdi, 2019. "Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources," Sustainability, MDPI, vol. 11(14), pages 1-28, July.
    5. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    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. Siddique Akbar & Toomas Vaimann & Bilal Asad & Ants Kallaste & Muhammad Usman Sardar & Karolina Kudelina, 2023. "State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions," Energies, MDPI, vol. 16(17), pages 1-44, September.
    2. Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.

    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. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    2. Jiao Liu & Jinfu Liu & Daren Yu & Myeongsu Kang & Weizhong Yan & Zhongqi Wang & Michael G. Pecht, 2018. "Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network," Energies, MDPI, vol. 11(8), pages 1-18, August.
    3. Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.
    4. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    5. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    6. Abbas Rabiee & Ali Abdali & Seyed Masoud Mohseni-Bonab & Mohsen Hazrati, 2021. "Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
    7. Feng Lu & Chunyu Jiang & Jinquan Huang & Yafan Wang & Chengxin You, 2016. "A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis," Energies, MDPI, vol. 9(10), pages 1-22, October.
    8. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    9. Yunpeng Cao & Xinran Lv & Guodong Han & Junqi Luan & Shuying Li, 2019. "Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network," Energies, MDPI, vol. 12(24), pages 1-17, December.
    10. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
    11. Hui Wang & Jingxuan Sun & Jianbo Sun & Jilong Wang, 2017. "Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    12. Xin Li & Fengrong Bi & Lipeng Zhang & Xiao Yang & Guichang Zhang, 2022. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer," Energies, MDPI, vol. 15(3), pages 1-17, February.
    13. Kiki Ayu & Akilu Yunusa-Kaltungo, 2020. "A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems," Energies, MDPI, vol. 13(8), pages 1-32, April.
    14. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.
    15. Kang, Do Won & Kim, Tong Seop, 2018. "Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation," Applied Energy, Elsevier, vol. 212(C), pages 1345-1359.
    16. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).
    17. Upasana Lakhina & Nasreen Badruddin & Irraivan Elamvazuthi & Ajay Jangra & Truong Hoang Bao Huy & Josep M. Guerrero, 2023. "An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
    18. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    19. Ming Cheng & Qiang Zhang & Yue Cao, 2024. "An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model," Energies, MDPI, vol. 17(15), pages 1-16, July.
    20. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).

    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:jsusta:v:11:y:2019:i:22:p:6202-:d:284088. 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.