Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach. (May 2018)
- Record Type:
- Journal Article
- Title:
- Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach. (May 2018)
- Main Title:
- Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach
- Authors:
- Lu, Yang
Sun, Liping
Zhang, Xinyue
Feng, Feng
Kang, Jichuan
Fu, Guoqiang - Abstract:
- Highlights: A condition-based maintenance optimization approach for offshore wind turbine systems considering opportunities is provided. An artificial neural network is adopted to predict the life percentage by leveraging the condition monitoring information. A conditional failure probability value is adopted to represent the deterioration of offshore wind turbine systems. A two-level failure probability threshold is utilized to define the proposed condition-based maintenance policy. Abstract: A well-established condition-based maintenance (CBM) method based on condition monitoring information can be used to reduce maintenance costs by decimating unnecessary maintenance actions, reducing system downtime, and minimizing unexpected failures. In this paper, we propose an opportunistic CBM optimization approach for offshore wind turbines (OWTs) in which economic dependence exists among the components that are subjected to condition monitoring. An artificial neural network is used to predict life percentage by leveraging the condition monitoring information. A conditional failure probability value that is derived from the predicted failure-time distribution of the component was adopted to represent the deterioration of OWTs. Our maintenance method can be defined by a threshold with two-level failure probability. We propose a simulation method that can be used to calculate the optimal threshold values to minimize the long-term maintenance cost. Failure information and maintenanceHighlights: A condition-based maintenance optimization approach for offshore wind turbine systems considering opportunities is provided. An artificial neural network is adopted to predict the life percentage by leveraging the condition monitoring information. A conditional failure probability value is adopted to represent the deterioration of offshore wind turbine systems. A two-level failure probability threshold is utilized to define the proposed condition-based maintenance policy. Abstract: A well-established condition-based maintenance (CBM) method based on condition monitoring information can be used to reduce maintenance costs by decimating unnecessary maintenance actions, reducing system downtime, and minimizing unexpected failures. In this paper, we propose an opportunistic CBM optimization approach for offshore wind turbines (OWTs) in which economic dependence exists among the components that are subjected to condition monitoring. An artificial neural network is used to predict life percentage by leveraging the condition monitoring information. A conditional failure probability value that is derived from the predicted failure-time distribution of the component was adopted to represent the deterioration of OWTs. Our maintenance method can be defined by a threshold with two-level failure probability. We propose a simulation method that can be used to calculate the optimal threshold values to minimize the long-term maintenance cost. Failure information and maintenance cost of OWTs are collected from existing articles to illustrate the proposed approach. Results show that the opportunistic CBM strategy can be effective and is established in the wind power industry. Moreover, the expense comparison between onshore and offshore WTs demonstrates the importance of this method. … (more)
- Is Part Of:
- Applied ocean research. Volume 74(2018)
- Journal:
- Applied ocean research
- Issue:
- Volume 74(2018)
- Issue Display:
- Volume 74, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 74
- Issue:
- 2018
- Issue Sort Value:
- 2018-0074-2018-0000
- Page Start:
- 69
- Page End:
- 79
- Publication Date:
- 2018-05
- Subjects:
- Condition based maintenance -- Offshore wind turbine -- Artificial neural network -- Opportunistic maintenance
Ocean engineering -- Periodicals
620.416205 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01411187 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.apor.2018.02.016 ↗
- Languages:
- English
- ISSNs:
- 0141-1187
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1576.240000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17962.xml