Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model

Jianlan Li, Xuran Zhang, Xing Zhou and Luyi Lu

Renewable Energy, 2019, vol. 132, issue C, 1076-1087

Abstract: Wind power develops very quickly in last decade to overcome the energy crisis and environment crisis. Mechanical components of wind turbines usually have characteristic with performance degradation that results in the declining reliability over time. Generally, the reliability data of equipment come from statistical analysis based on extensive experiments and operations. However, wind turbines, as expensive large-scale equipment with long lifetime, face with the dilemma of lacking enough statistical data, and leads to insufficiency reliability data for field operations and thus results in frequent wind turbine faults. A new reliability assessment method based on Hidden-Markov model considering performance degradation, called degradation-Hidden-Markov model, is proposed in this paper. The performance degradation rule of wind turbine component is derived using the monitoring data of performance parameters. Hidden-Markov model is improved by the performance degradation rule of the component to create a new time-correlated state transition probability matrix with degradation feature. The reliability curve is obtained using the state probabilities of the degradation-Hidden-Markov model. Thus, the presented method realizes the reliability assessment of component based on small sample data of wind turbine. Finally, the reliability assessment of a gearbox bearing of a 1.5 MW wind turbine by the degradation-Hidden-Markov model proves its validity.

Keywords: Wind turbine; Bearing; Performance degradation; Degradation-hidden-markov; Reliability assessment (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:132:y:2019:i:c:p:1076-1087

DOI: 10.1016/j.renene.2018.08.048

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