Dynamic maintenance planning of a hydro-turbine in operational life cycle. (December 2020)

• Record Type: Journal Article
• Authors: Li, Ruopu; Arzaghi, Ehsan; Abbassi, Rouzbeh; Chen, Diyi; Li, Chunhao; Li, Huanhuan; Xu, Beibei
• Is Part Of: Reliability engineering & system safety. Volume 204(2020)
• Publication Date: 2020-12
• Subjects: Hydro-turbine -- Life cycle assessment -- Hidden Markov model -- Dynamic maintenance; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2020.107129
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A hidden Markov model with the monitoring data of hydro-turbine runner cracks from actual operations is proposed for the life cycle assessment (LCA). The new maintenance interval is formulated and the inspection times are defined based on LCA. The optimal maintenance plans are developed by analyzing the influence of cracks being in different states leading to a hazardous condition. 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.