LAPSE:2023.18556
Published Article

LAPSE:2023.18556
Updatable Probabilistic Evaluation of Failure Rates of Mechanical Components in Power Take-Off Systems of Tidal Stream Turbines
March 8, 2023
Abstract
This paper presents a method for the probabilistic evaluation of the failure rates of mechanical components in a typical power take-off (PTO) system of a horizontal-axis tidal stream turbine (HATT). The method is based on a modification of the method of the influence factors, when base failure rates, relevant influence factors and, subsequently, resulting failure rates are treated as random variables. The prior (i.e., initial) probabilistic distribution of the failure rates of a HATT component is generated using data for similar components from other industries, while taking into account actual characteristics of the component and site-specific operating and environmental conditions of the HATT. A posterior distribution of the failure rate is estimated numerically based on a Bayesian approach as new information about the component performance in an operating HATT becomes available. The posterior distribution is then employed to obtain the updated mean and lower and upper confidence limits of the failure rate. The proposed method is illustrated by applying it to the evaluation of the failure rates of two key components of the PTO system of a typical HATT—main seal and main bearing. In particular, it is shown that uncertainty associated with the method itself has a major influence on the failure rate evaluation. The proposed method is useful for the reliability assessment of both PTO designs of new HATTs and PTO systems of operating HATTs.
This paper presents a method for the probabilistic evaluation of the failure rates of mechanical components in a typical power take-off (PTO) system of a horizontal-axis tidal stream turbine (HATT). The method is based on a modification of the method of the influence factors, when base failure rates, relevant influence factors and, subsequently, resulting failure rates are treated as random variables. The prior (i.e., initial) probabilistic distribution of the failure rates of a HATT component is generated using data for similar components from other industries, while taking into account actual characteristics of the component and site-specific operating and environmental conditions of the HATT. A posterior distribution of the failure rate is estimated numerically based on a Bayesian approach as new information about the component performance in an operating HATT becomes available. The posterior distribution is then employed to obtain the updated mean and lower and upper confidence limits of the failure rate. The proposed method is illustrated by applying it to the evaluation of the failure rates of two key components of the PTO system of a typical HATT—main seal and main bearing. In particular, it is shown that uncertainty associated with the method itself has a major influence on the failure rate evaluation. The proposed method is useful for the reliability assessment of both PTO designs of new HATTs and PTO systems of operating HATTs.
Record ID
Keywords
Bayesian analysis, failure rate, mechanical components, probabilistic analysis, reliability, tidal stream turbine
Subject
Suggested Citation
Val DV, Chernin L, Yurchenko D. Updatable Probabilistic Evaluation of Failure Rates of Mechanical Components in Power Take-Off Systems of Tidal Stream Turbines. (2023). LAPSE:2023.18556
Author Affiliations
Val DV: Institute for Infrastructure & Environment, Heriot-Watt University, Edinburgh EH14 4AS, UK [ORCID]
Chernin L: School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK [ORCID]
Yurchenko D: Institute of Mechanical, Process & Energy Engineering, Heriot-Watt University, Edinburgh EH14 4AS, UK [ORCID]
Chernin L: School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK [ORCID]
Yurchenko D: Institute of Mechanical, Process & Energy Engineering, Heriot-Watt University, Edinburgh EH14 4AS, UK [ORCID]
Journal Name
Energies
Volume
14
Issue
20
First Page
6586
Year
2021
Publication Date
2021-10-13
ISSN
1996-1073
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Original Submission
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PII: en14206586, Publication Type: Journal Article
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LAPSE:2023.18556
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https://doi.org/10.3390/en14206586
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Mar 8, 2023
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