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Dynamic maintenance planning of a hydro-turbine in operational life cycle

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  • Li, Ruopu
  • Arzaghi, Ehsan
  • Abbassi, Rouzbeh
  • Chen, Diyi
  • Li, Chunhao
  • Li, Huanhuan
  • Xu, Beibei

Abstract

Life cycle assessment (LCA) has been an emerging feature of modern structural health monitoring techniques that aims at evaluating equipment degradation process and it can be used for development of dynamic maintenance plans. A novel framework based on LCA is proposed in this paper for dynamic maintenance planning of hydro-turbines. Using a Hidden Markov Model (HMM) and the inspection data of hydro-turbine runner cracks from actual operations, the transition probability matrix and sojourn time of different crack states are obtained. This is utilized for predicting the expected remaining useful life (RUL) and the formulation of maintenance intervals. Moreover, maintenance plans are developed from the influence of cracks in different states leading to critical conditions. The proposed method, as demonstrated in the case study, efficiently updates the maintenance intervals and reduces the likelihood of failure events. The results of this paper provide a valid maintenance framework for achieving higher operational safety of hydropower stations with minimal resource losses.

Suggested Citation

  • Li, Ruopu & Arzaghi, Ehsan & Abbassi, Rouzbeh & Chen, Diyi & Li, Chunhao & Li, Huanhuan & Xu, Beibei, 2020. "Dynamic maintenance planning of a hydro-turbine in operational life cycle," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s095183202030630x
    DOI: 10.1016/j.ress.2020.107129
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    2. Gámiz, M.L. & Navas-Gómez, F. & Raya-Miranda, R. & Segovia-García, M.C., 2023. "Dynamic reliability and sensitivity analysis based on HMM models with Markovian signal process," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    3. BULUT, Merve & ÖZCAN, Evrencan, 2021. "A new approach to determine maintenance periods of the most critical hydroelectric power plant equipment," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    4. Kang, Renwei & Wang, Junfeng & Chen, Jianqiu & Zhou, Jingjing & Pang, Yanzhi & Guo, Longlong & Cheng, Jianfeng, 2022. "A method of online anomaly perception and failure prediction for high-speed automatic train protection system," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Vega, Manuel A. & Hu, Zhen & Fillmore, Travis B. & Smith, Matthew D. & Todd, Michael D., 2021. "A Novel Framework for Integration of Abstracted Inspection Data and Structural Health Monitoring for Damage Prognosis of Miter Gates," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    6. Fawaz E. Alsaadi & Amirreza Yasami & Hajid Alsubaie & Ahmed Alotaibi & Hadi Jahanshahi, 2022. "Control of a Hydraulic Generator Regulating System Using Chebyshev-Neural-Network-Based Non-Singular Fast Terminal Sliding Mode Method," Mathematics, MDPI, vol. 11(1), pages 1-18, December.
    7. Sara Antomarioni & Marjorie Maria Bellinello & Maurizio Bevilacqua & Filippo Emanuele Ciarapica & Renan Favarão da Silva & Gilberto Francisco Martha de Souza, 2020. "A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant," Energies, MDPI, vol. 13(23), pages 1-16, December.
    8. Guo, Chunhui & Liang, Zhenglin, 2022. "A predictive Markov decision process for optimizing inspection and maintenance strategies of partially observable multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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