IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2020i1p13-d466445.html
   My bibliography  Save this article

Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine

Author

Listed:
  • Mingliang Bai

    (Harbin Institute of Technology, Harbin 150001, China)

  • Jinfu Liu

    (Harbin Institute of Technology, Harbin 150001, China)

  • Yujia Ma

    (Harbin Institute of Technology, Harbin 150001, China)

  • Xinyu Zhao

    (Harbin Institute of Technology, Harbin 150001, China)

  • Zhenhua Long

    (Harbin Institute of Technology, Harbin 150001, China)

  • Daren Yu

    (Harbin Institute of Technology, Harbin 150001, China)

Abstract

Fault detection and diagnosis can improve safety and reliability of gas turbines. Current studies on gas turbine fault detection and diagnosis mainly focus on the case of abundant fault samples. However, fault data are rare or even unavailable for gas turbines, especially newly-run gas turbines. Aiming to realize fault detection with only normal data, this paper proposes the concept of normal pattern group. A group of long-short term memory (LSTM) networks are first used for characterizing the mapping relationships among measurable parameters of healthy three-shaft gas turbines. Experiments show that the proposed method can detect all 13 common gas path faults of three-shaft gas turbines sensitively while remaining low false alarm rate. Comparison experiment with single normal pattern model verifies the necessaries and superiorities of using normal pattern group. Meanwhile, comparison between LSTM network and other methods including support vector regression, single-layer feedforward neural network, extreme learning machine and Elman recurrent neural network verifies the superiorities of LSTM network in fault detection. Furthermore, comparison experiment with four common one-class classifiers further verifies the superiorities of the proposed method. This also indicates the superiorities of data-driven methods and gas turbine principle fusion to some extent.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:13-:d:466445
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/1/13/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/1/13/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    2. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    3. Fast, M. & Palmé, T., 2010. "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant," Energy, Elsevier, vol. 35(2), pages 1114-1120.
    4. 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.
    5. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    6. Zhi-tao Wang & Ning-bo Zhao & Wei-ying Wang & Rui Tang & Shu-ying Li, 2015. "A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, June.
    7. 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.
    8. Orozco, Dimas José Rúa & Venturini, Osvaldo José & Escobar Palacio, José Carlos & del Olmo, Oscar Almazán, 2017. "A new methodology of thermodynamic diagnosis, using the thermoeconomic method together with an artificial neural network (ANN): A case study of an externally fired gas turbine (EFGT)," Energy, Elsevier, vol. 123(C), pages 20-35.
    9. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. 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. Aygun, Hakan & Dursun, Omer Osman & Toraman, Suat, 2023. "Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes," Energy, Elsevier, vol. 271(C).
    2. 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.

    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. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    2. Martí de Castro-Cros & Manel Velasco & Cecilio Angulo, 2021. "Machine-Learning-Based Condition Assessment of Gas Turbines—A Review," Energies, MDPI, vol. 14(24), pages 1-27, December.
    3. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    4. 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).
    5. Jinfu Liu & Zhenhua Long & Mingliang Bai & Linhai Zhu & Daren Yu, 2021. "A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems," Energies, MDPI, vol. 14(2), pages 1-31, January.
    6. Park, Yeseul & Choi, Minsung & Choi, Gyungmin, 2022. "Fault detection of industrial large-scale gas turbine for fuel distribution characteristics in start-up procedure using artificial neural network method," Energy, Elsevier, vol. 251(C).
    7. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    8. Long, Zhenhua & Bai, Mingliang & Ren, Minghao & Liu, Jinfu & Yu, Daren, 2023. "Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network," Energy, Elsevier, vol. 272(C).
    9. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    10. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    11. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    12. Zhao, Junjie & Li, Yi-Guang & Sampath, Suresh, 2023. "A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics," Applied Energy, Elsevier, vol. 332(C).
    13. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
    14. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).
    15. Angjun Xie & Gang Xu & Chunming Nie & Heng Chen & Tailaiti Tuerhong, 2024. "Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms," Energies, MDPI, vol. 17(22), pages 1-23, November.
    16. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    17. 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).
    18. Kim, Minsoo & Park, Taeseop & Jeong, Jaeik & Kim, Hongseok, 2023. "Stochastic optimization of home energy management system using clustered quantile scenario reduction," Applied Energy, Elsevier, vol. 349(C).
    19. Xilong Lin & Yisen Niu & Zixuan Yan & Lianglin Zou & Ping Tang & Jifeng Song, 2024. "Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    20. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(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:14:y:2020:i:1:p:13-:d:466445. 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.