Supporting group maintenance through prognostics-enhanced dynamic dependability prediction
- Submitting institution
-
The University of Hull
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1397651
- Type
- D - Journal article
- DOI
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10.1016/j.ress.2017.04.005
- Title of journal
- Reliability Engineering and System Safety
- Article number
- -
- First page
- 171
- Volume
- 168
- Issue
- -
- ISSN
- 0951-8320
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- URL
-
http://www.sciencedirect.com/science/article/pii/S0951832016308626
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
9
- Research group(s)
-
-
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents an online system maintenance method that takes into account system dynamics to enable the optimisation of maintenance planning in the HiP-HOPS method and tool. It employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets to provide a cost-effective approach to asset management. The paper also provides a basis for further green energy initiatives in the Data-driven Reliability-centred Evolutionary Automated Maintenance for Offshore Wind Farms (DREAM) project, which is funded by EDF-London Research (https://yipapadopoulos.wixsite.com/yiap/dream). The paper contributes to the BIOLOGIC ICS.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -