IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i5p724-d99215.html
   My bibliography  Save this article

Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method

Author

Listed:
  • Fei Li

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Hongzhi Wang

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Guowen Zhou

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Daren Yu

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Jiangzhong Li

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

  • Hong Gao

    (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)

Abstract

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering the collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is used to evaluate posterior probabilities of observing symbolic sequences and the most probable state sequences they may locate. Hence an estimation-based model and a decoding-based model are used to identify anomalies in two different ways. Experimental results indicate that both models have both ideal performance overall, but the estimation-based model has a strong robustness ability, whereas the decoding-based model has a strong accuracy ability, particularly in a certain range of sequence lengths. Therefore, the proposed method can facilitate well existing symbolic dynamic analysis- based anomaly detection methods, especially in the gas turbine domain.

Suggested Citation

  • Fei Li & Hongzhi Wang & Guowen Zhou & Daren Yu & Jiangzhong Li & Hong Gao, 2017. "Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method," Energies, MDPI, vol. 10(5), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:724-:d:99215
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/5/724/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/5/724/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marinai, Luca & Probert, Douglas & Singh, Riti, 2004. "Prospects for aero gas-turbine diagnostics: a review," Applied Energy, Elsevier, vol. 79(1), pages 109-126, 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. 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.
    2. Israel Reyes-Ramírez & Santiago D. Martínez-Boggio & Pedro L. Curto-Risso & Alejandro Medina & Antonio Calvo Hernández & Lev Guzmán-Vargas, 2018. "Symbolic Analysis of the Cycle-to-Cycle Variability of a Gasoline–Hydrogen Fueled Spark Engine Model," Energies, MDPI, vol. 11(4), pages 1-19, April.
    3. Moghaddass, Ramin & Sheng, Shuangwen, 2019. "An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework," Applied Energy, Elsevier, vol. 240(C), pages 561-582.

    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. 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.
    2. Yamamoto, Satoru & Uemura, Akihiro & Miyazawa, Hironori & Furusawa, Takashi & Yonezawa, Koichi & Umezawa, Shuichi & Ohmori, Shuichi & Suzuki, Takeshi, 2020. "A numerical and analytical coupling method for predicting the performance of intermediate-pressure steam turbines in operation," Energy, Elsevier, vol. 198(C).
    3. Muhammad Baqir Hashmi & Mohammad Mansouri & Amare Desalegn Fentaye & Shazaib Ahsan & Konstantinos Kyprianidis, 2024. "An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines," Energies, MDPI, vol. 17(3), pages 1-23, February.
    4. Nalianda, D.K. & Kyprianidis, K.G. & Sethi, V. & Singh, R., 2015. "Techno-economic viability assessments of greener propulsion technology under potential environmental regulatory policy scenarios," Applied Energy, Elsevier, vol. 157(C), pages 35-50.
    5. Kyprianidis, Konstantinos G. & Dahlquist, Erik, 2017. "On the trade-off between aviation NOx and energy efficiency," Applied Energy, Elsevier, vol. 185(P2), pages 1506-1516.
    6. Maria Grazia De Giorgi & Nicola Menga & Antonio Ficarella, 2023. "Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies," Energies, MDPI, vol. 16(6), pages 1-37, March.
    7. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    8. Dong, Keqiang & Long, Linan & Zhang, Hong & Gao, You, 2018. "The mutual information based minimum spanning tree to detect and evaluate dependencies between aero-engine gas path system variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 248-253.
    9. 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.
    10. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).

    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:5:p:724-:d:99215. 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.