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Condition-based maintenance is applied in various industries to monitor and control critical assets and to optimize maintenance efforts. Its applicability to the offshore wind energy industry has been considered for almost 20 years and has resulted in the development and implementation of solutions that have contributed to lower cost of maintenance and increased asset availability. However, there is currently no public domain guidance available that provides the information required to (i) prioritize systems for which condition monitoring would generate highest value and to (ii) understand the parameters that need to be monitored by a specific system from failure cause to failure mode. Both items are addressed in this paper, providing a clearly structured, risk-based assessment methodology and corresponding results for state-of-the-art offshore wind turbines. A total of 337 failure modes have been identified and analysed by experts representing approximately 70% of the European offshore wind market to assess potential benefits of condition monitoring systems. Results may be used to target the development of condition monitoring systems focusing on critical systems and to find optimal O&M strategies by understanding failure paths of main offshore wind turbine systems resulting in a lower cost of energy and a more optimal risk-return balance.
690, Hydraulic engineering. Ocean engineering, TC
690, Hydraulic engineering. Ocean engineering, TC
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 111 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |