IDEAS home Printed from https://ideas.repec.org/a/bpj/jossai/v4y2016i4p354-364n6.html
   My bibliography  Save this article

Reliability Analysis of Multi-State Engine Units Utilizing Time-Domain Response Data

Author

Listed:
  • FANG Yongfeng
  • TAO Wenliang

    (School of Electronic & Mechanical Engineering, Guizhou University of Science Technology, Bijie551700, China)

  • TEE Kong Fah

    (Department of Engineering Science, University of Greenwich, KentME44TB, United Kingdom)

Abstract

A novel reliability-based approach has been developed for multi-state engine systems. Firstly, the output power of the engine is discretized and modeled as a discrete-state continuous-time Markov random process. Secondly, the multi-state Markov model is established. According to the observed data, the transition intensity is determined. Thirdly, the proposed method is extended to compute the forced outage rate and the expected engine capacity deficiency based on time response. The proposed method can therefore be used for forecasting and monitoring the reliability of the multi-state engine utilizing time-domain response data. It is illustrated that the proposed method is practicable, feasible and gives reasonable prediction which conforms to the engineering practice.

Suggested Citation

  • FANG Yongfeng & TAO Wenliang & TEE Kong Fah, 2016. "Reliability Analysis of Multi-State Engine Units Utilizing Time-Domain Response Data," Journal of Systems Science and Information, De Gruyter, vol. 4(4), pages 354-364, August.
    • Handle: RePEc:bpj:jossai:v:4:y:2016:i:4:p:354-364:n:6
    DOI: 10.21078/JSSI-2016-354-11
    as

    Download full text from publisher

    File URL: https://doi.org/10.21078/JSSI-2016-354-11
    Download Restriction: no
    File URL: https://libkey.io/10.21078/JSSI-2016-354-11?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. Lisnianski, Anatoly & Elmakias, David & Laredo, David & Ben Haim, Hanoch, 2012. "A multi-state Markov model for a short-term reliability analysis of a power generating unit," Reliability Engineering and System Safety, Elsevier, vol. 98(1), pages 1-6.
    2. Roy Billinton & Yi Gao & Dange Huang & Rajesh Karki, 2011. "Adequacy Assessment of Wind-Integrated Composite Generation and Transmission Systems," Springer Series in Reliability Engineering, in: George Anders & Alfredo Vaccaro (ed.), Innovations in Power Systems Reliability, pages 141-167, Springer.
    Full references (including those not matched with items on IDEAS)

    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. Zhang, Cai Wen & Zhang, Tieling & Chen, Nan & Jin, Tongdan, 2013. "Reliability modeling and analysis for a novel design of modular converter system of wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 86-94.
    2. Farag Ali El-Sheikhi & Hisham M. Soliman & Razzaqul Ahshan & Eklas Hossain, 2021. "Regional Pole Placers of Power Systems under Random Failures/Repair Markov Jumps," Energies, MDPI, vol. 14(7), pages 1-14, April.
    3. Song, Haifeng & Liu, Jieyu & Schnieder, Eckehard, 2017. "Validation, verification and evaluation of a Train to Train Distance Measurement System by means of Colored Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 10-23.
    4. Fazlollahtabar, Hamed & Saidi-Mehrabad, Mohammad & Balakrishnan, Jaydeep, 2015. "Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 34-44.
    5. D׳Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Reliability measures for indexed semi-Markov chains applied to wind energy production," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 170-177.
    6. Postnikov, Ivan & Stennikov, Valery & Mednikova, Ekaterina & Penkovskii, Andrey, 2018. "Methodology for optimization of component reliability of heat supply systems," Applied Energy, Elsevier, vol. 227(C), pages 365-374.
    7. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    8. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    9. Zhou, Taotao & Zhang, Xiaoge & Droguett, Enrique Lopez & Mosleh, Ali, 2023. "A generic physics-informed neural network-based framework for reliability assessment of multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    10. Eldosouky, AbdelRahman & Saad, Walid & Mandayam, Narayan, 2021. "Resilient critical infrastructure: Bayesian network analysis and contract-Based optimization," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    11. Panagiotis K. Marhavilas & Michael G. Tegas & Georgios K. Koulinas & Dimitrios E. Koulouriotis, 2020. "A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study," Sustainability, MDPI, vol. 12(10), pages 1-21, May.
    12. Chen, Qian & Zuo, Lili & Wu, Changchun & Li, Yun & Hua, Kaixun & Mehrtash, Mahdi & Cao, Yankai, 2022. "Optimization of compressor standby schemes for gas transmission pipeline systems based on gas delivery reliability," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    13. Zhao, Yunfei & Gao, Wei & Smidts, Carol, 2021. "Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    14. Song, Xiaogang & Zhai, Zhengjun & Liu, Yidong & Han, Jie, 2018. "A stochastic approach for the reliability evaluation of multi-state systems with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 257-266.
    15. Kong, Yaonan & Ye, Zhisheng, 2017. "Goodness-of-fit tests in the multi-state Markov model," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 16-24.
    16. Ivan Postnikov & Ekaterina Samarkina & Andrey Penkovskii & Vladimir Kornev & Denis Sidorov, 2023. "Modeling Unpredictable Behavior of Energy Facilities to Ensure Reliable Operation in a Cyber-Physical System," Energies, MDPI, vol. 16(19), pages 1-11, October.
    17. Zhang, Aibo & Wu, Shengnan & Fan, Dongming & Xie, Min & Cai, Baoping & Liu, Yiliu, 2022. "Adaptive testing policy for multi-state systems with application to the degrading final elements in safety-instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    18. Chun-Ho Wang & Chao-Hui Huang & Deng-Guei You, 2022. "Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand," Sustainability, MDPI, vol. 14(13), pages 1-29, June.
    19. Dhople, S.V. & DeVille, L. & Domínguez-García, A.D., 2014. "A Stochastic Hybrid Systems framework for analysis of Markov reward models," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 158-170.
    20. Postnikov, Ivan, 2022. "A reliability assessment of the heating from a hybrid energy source based on combined heat and power and wind power plants," Reliability Engineering and System Safety, Elsevier, vol. 221(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:bpj:jossai:v:4:y:2016:i:4:p:354-364:n:6. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.