IDEAS home Printed from https://ideas.repec.org/a/hin/complx/4927453.html
   My bibliography  Save this article

A Reduced-Order Fault Detection Filtering Approach for Continuous-Time Markovian Jump Systems with Polytopic Uncertainties

Author

Listed:
  • Lihong Rong
  • Xiuyan Peng
  • Biao Zhang

Abstract

The fault detection (FD) reduced-order filtering problem is investigated for a family of continuous-time Markovian jump linear systems (MJLSs) with polytopic uncertain transition rates, which also include the totally known and partly unknown transition rates. Then, in accordance with the convexification techniques, a novel sufficient condition for the existence of FD reduced-order filter over MJLSs with deficient transition information is obtained in terms of linear matrix inequality (LMI), which can ensure the error augmented system with the FD reduced-order filter is randomly stable. In addition, a performance index is given to enhance the robustness of the residual system against deficient transition information and external disturbance, such that the error between the fault and the residual is made as small as possible to reinforce the faults sensitivity. Finally, the effectiveness of the proposed method is substantiated with two illustrative examples.

Suggested Citation

  • Lihong Rong & Xiuyan Peng & Biao Zhang, 2017. "A Reduced-Order Fault Detection Filtering Approach for Continuous-Time Markovian Jump Systems with Polytopic Uncertainties," Complexity, Hindawi, vol. 2017, pages 1-14, January.
  • Handle: RePEc:hin:complx:4927453
    DOI: 10.1155/2017/4927453
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2017/4927453.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2017/4927453.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2017/4927453?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    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. Li Feng & Ke Zhang & Yi Chai & Shuiqing Xu & Zhimin Yang, 2017. "Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length," Complexity, Hindawi, vol. 2017, pages 1-9, November.
    2. Mohanapriya, S. & Sweety, C. Antony Crispin & Sakthivel, R. & Parthasarathy, V., 2023. "Disturbance attenuation for neutral Markovian jump systems with multiple delays," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).

    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. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.
    2. Le Zhang & Qiang Yang, 2020. "Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox," Energies, MDPI, vol. 13(2), pages 1-13, January.
    3. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    4. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    5. Moynihan, Bridget & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2022. "Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification," Renewable Energy, Elsevier, vol. 184(C), pages 662-676.
    6. Taylor, Josh A. & Dhople, Sairaj V. & Callaway, Duncan S., 2016. "Power systems without fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1322-1336.
    7. Hare, James & Shi, Xiaofang & Gupta, Shalabh & Bazzi, Ali, 2016. "Fault diagnostics in smart micro-grids: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1114-1124.
    8. Wenyi, Liu & Zhenfeng, Wang & Jiguang, Han & Guangfeng, Wang, 2013. "Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM," Renewable Energy, Elsevier, vol. 50(C), pages 1-6.
    9. Harrold, Magnus & Ouro, Pablo & O’Doherty, Tim, 2020. "Performance assessment of a tidal turbine using two flow references," Renewable Energy, Elsevier, vol. 153(C), pages 624-633.
    10. Papini, Guglielmo & Faedo, Nicolás & Mattiazzo, Giuliana, 2024. "Fault diagnosis and fault-tolerant control in wave energy: A perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    11. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    12. Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
    13. Guerine, A. & El Hami, A. & Walha, L. & Fakhfakh, T. & Haddar, M., 2017. "Dynamic response of wind turbine gear system with uncertain-but-bounded parameters using interval analysis method," Renewable Energy, Elsevier, vol. 113(C), pages 679-687.
    14. Oh, Ki-Yong & Park, Joon-Young & Lee, Jun-Shin & Lee, JaeKyung, 2015. "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy, Elsevier, vol. 79(C), pages 150-160.
    15. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    16. Lee, Jae-Kyung & Park, Joon-Young & Oh, Ki-Yong & Ju, Seung-Hwan & Lee, Jun-Shin, 2015. "Transformation algorithm of wind turbine blade moment signals for blade condition monitoring," Renewable Energy, Elsevier, vol. 79(C), pages 209-218.
    17. Yunxuan Dong & Jianzhou Wang & Chen Wang & Zhenhai Guo, 2017. "Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting," Energies, MDPI, vol. 10(4), pages 1-27, April.
    18. Rodríguez-López, Miguel A. & López-González, Luis M. & López-Ochoa, Luis M. & Las-Heras-Casas, Jesús, 2016. "Development of indicators for the detection of equipment malfunctions and degradation estimation based on digital signals (alarms and events) from operation SCADA," Renewable Energy, Elsevier, vol. 99(C), pages 224-236.
    19. Kong, Yun & Wang, Tianyang & Feng, Zhipeng & Chu, Fulei, 2020. "Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine," Renewable Energy, Elsevier, vol. 152(C), pages 754-769.
    20. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.

    More about this item

    Statistics

    Access and download statistics

    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:hin:complx:4927453. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.