Improved dynamic dependability assessment through integration with prognostics

Aizpurua, J. I.; Catterson, V. M.; Papadopoulos, Y.; Chiacchio, F.; Manno, G.

Abstract

The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognostics updated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset interdependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations.

Citation

Aizpurua, J. I., Catterson, V. M., Papadopoulos, Y., Chiacchio, F., & Manno, G. (2017). Improved dynamic dependability assessment through integration with prognostics. IEEE Transactions on Reliability, 66(3), 893-913. https://doi.org/10.1109/tr.2017.2693821

• Journal Article Type: Article
• Acceptance Date: Apr 9, 2017
• Online Publication Date: May 10, 2017
• Publication Date: 2017-09
• Deposit Date: May 23, 2017
• Publicly Available Date: May 23, 2017
• Journal: IEEE transactions on reliability
• Print ISSN: 0018-9529
• Electronic ISSN: 1558-1721
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 66
• Issue: 3
• Pages: 893-913
• DOI: https://doi.org/10.1109/tr.2017.2693821
• Keywords: Prognostics; Dynamic dependability; Model to model transformation; Risk monitor; Remaining useful life; Condition monitoring
• Public URL: https://hull-repository.worktribe.com/output/451549
• Publisher URL: http://ieeexplore.ieee.org/document/7924411/
• Additional Information: This is the accepted manuscript of an article published in: IEEE transactions on reliability, 2017, v.66, issue 3.