IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3414-d920007.html
   My bibliography  Save this article

Simultaneous-Fault Diagnosis of Satellite Power System Based on Fuzzy Neighborhood ζ -Decision-Theoretic Rough Set

Author

Listed:
  • Laifa Tao

    (Institute of Reliability Engineering, Beihang University, Beijing 100191, China
    Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing 100191, China
    School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Chao Wang

    (Institute of Reliability Engineering, Beihang University, Beijing 100191, China
    Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing 100191, China
    School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Yuan Jia

    (Beijing Institute of Radio Metrology and Measurement, China Aerospace Science and Industry Corporation Limited, Beijing 100039, China)

  • Ruzhi Zhou

    (Shanghai Institute of Satellite Engineering, China Aerospace Science and Technology Corporation, Shanghai 201109, China)

  • Tong Zhang

    (Marine Design and Research Institute of China, China State Shipbuilding Corporation Limited, Shanghai 200011, China)

  • Yiling Chen

    (Institute of Reliability Engineering, Beihang University, Beijing 100191, China
    Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing 100191, China
    School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Chen Lu

    (Institute of Reliability Engineering, Beihang University, Beijing 100191, China
    Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing 100191, China
    School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

  • Mingliang Suo

    (Institute of Reliability Engineering, Beihang University, Beijing 100191, China
    Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing 100191, China
    School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)

Abstract

Due to the increasing complexity of the entire satellite system and the deteriorating orbital environment, multiple independent single faults may occur simultaneously in the satellite power system. However, two stumbling blocks hinder the effective diagnosis of simultaneous-fault, namely, the difficulty of obtaining the simultaneous-fault data and the extremely complicated mapping of the simultaneous-fault modes to the sensor data. To tackle the challenges, a fault diagnosis strategy based on a novel rough set model is proposed. Specifically, a novel rough set model named FN ζ DTRS by introducing a concise loss function matrix and fuzzy neighborhood relationship is proposed to accurately mine and characterize the relationship between fault and data. Furthermore, an attribute rule-based fault matching strategy is designed without using simultaneous-fault data as training samples. The numerical experiments demonstrate the effectiveness of the FN ζ DTRS model, and the diagnosis experiments performed on a satellite power system illustrate the superiority of the proposed approach.

Suggested Citation

  • Laifa Tao & Chao Wang & Yuan Jia & Ruzhi Zhou & Tong Zhang & Yiling Chen & Chen Lu & Mingliang Suo, 2022. "Simultaneous-Fault Diagnosis of Satellite Power System Based on Fuzzy Neighborhood ζ -Decision-Theoretic Rough Set," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3414-:d:920007
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3414/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/19/3414/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Zehan & Li, Shuanghong & Xiao, Yawen & Yang, Yupu, 2019. "Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning," Applied Energy, Elsevier, vol. 233, pages 930-942.
    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. M. Ganesan & R. Lavanya, 2023. "Simultaneous fault detection in satellite power systems using deep autoencoders and classifier chain," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 83(1), pages 1-15, May.

    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. Xu, Yuan-wu & Wu, Xiao-long & Zhong, Xiao-bo & Zhao, Dong-qi & Sorrentino, Marco & Jiang, Jianhua & Jiang, Chang & Fu, Xiaowei & Li, Xi, 2021. "Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage," Applied Energy, Elsevier, vol. 286(C).
    2. Guk, Erdogan & Venkatesan, Vijay & Babar, Shumaila & Jackson, Lisa & Kim, Jung-Sik, 2019. "Parameters and their impacts on the temperature distribution and thermal gradient of solid oxide fuel cell," Applied Energy, Elsevier, vol. 241(C), pages 164-173.
    3. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    4. M. Ganesan & R. Lavanya, 2023. "Simultaneous fault detection in satellite power systems using deep autoencoders and classifier chain," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 83(1), pages 1-15, May.
    5. Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2021. "Working zone for a least-squares support vector machine for modeling polymer electrolyte fuel cell voltage," Applied Energy, Elsevier, vol. 283(C).
    6. 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).
    7. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.
    8. Pang, Ran & Zhang, Caizhi & Dai, Haifeng & Bai, Yunfeng & Hao, Dong & Chen, Jinrui & Zhang, Bin, 2022. "Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters," Applied Energy, Elsevier, vol. 305(C).
    9. Behzad Najafi & Paolo Bonomi & Andrea Casalegno & Fabio Rinaldi & Andrea Baricci, 2020. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests," Energies, MDPI, vol. 13(14), pages 1-19, July.
    10. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    11. Xie, Jiale & Xu, Jingfan & Wei, Zhongbao & Li, Xiaoyu, 2023. "Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network," Energy, Elsevier, vol. 263(PD).
    12. Zhong, Xiaobo & Xu, Yuanwu & Liu, Yanlin & Wu, Xiaolong & Zhao, Dongqi & Zheng, Yi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Root cause analysis and diagnosis of solid oxide fuel cell system oscillations based on data and topology-based model," Applied Energy, Elsevier, vol. 267(C).
    13. Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Kai Xiong & Chuangting Chen & Mumin Rao & Zhiping Peng & Xi Li & Jingxuan Peng, 2022. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems," Energies, MDPI, vol. 15(7), pages 1-16, March.
    14. Heng Zhang & Zhongyong Liu & Weilai Liu & Lei Mao, 2022. "Diagnosing Improper Membrane Water Content in Proton Exchange Membrane Fuel Cell Using Two-Dimensional Convolutional Neural Network," Energies, MDPI, vol. 15(12), pages 1-15, June.

    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:jmathe:v:10:y:2022:i:19:p:3414-:d:920007. 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.