TY - JOUR
T1 - Improved dynamic dependability assessment through integration with prognostics
AU - Aizpurua, Jose Ignacio
AU - Catterson, Victoria M.
AU - Papadopoulos, Yiannis
AU - Chiacchio, Ferdinando
AU - Manno, Gabriele
N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2017/5/10
Y1 - 2017/5/10
N2 - The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions.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 inter-dependencies, 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.
AB - The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions.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 inter-dependencies, 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.
KW - prognostics
KW - dynamic dependability
KW - model to model transformation
KW - risk transformation
KW - risk monitor
KW - remaining useful life
KW - condition monitoring
UR - http://ieeexplore.ieee.org/document/7924411/
U2 - 10.1109/TR.2017.2693821
DO - 10.1109/TR.2017.2693821
M3 - Article
SN - 0018-9529
SP - 1
EP - 21
JO - IEEE Transactions on Reliability
JF - IEEE Transactions on Reliability
ER -