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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Article number107129
Pages (from-to)1-11
Number of pages11
JournalReliability Engineering and System Safety
Publication statusPublished - Dec 2020


  • Hydro-turbine
  • Life cycle assessment
  • Hidden Markov model
  • Dynamic maintenance


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