TY - JOUR
T1 - On a simple scheme for systems modeling and identification using big data techniques
AU - Glavind, Sebastian T.
AU - Sepulveda, Juan G.
AU - Faber, Michael H.
N1 - Funding Information:
The authors gratefully acknowledge the funding received from Centre for Oil and Gas-DTU/Danish Hydrocarbon Research and Technology Centre (DHRTC) .
Publisher Copyright:
© 2021
PY - 2022/4
Y1 - 2022/4
N2 - In the field of reliability engineering and systems safety, it is a common challenge to identify the state of a system with basis in a limited set of observations of system performances. Often, there are a multitude of different possible system states, including states of damages, which compete in explaining the observations. To account for these in the context of risk-informed management of systems, the probabilities of the relevant possible different states are needed. In the present contribution, an idea on how this might be supported through big data techniques is presented. Here, systems are considered more holistically and not only as the relationship between input and output. The starting point is to establish a knowledge-consistent probabilistic representation of the system, its key performance characteristics, and the observations (exposures, condition state and performances) that may be collected from the system in reality. Monte Carlo simulations are then employed to establish the relevant scenarios of realizations of the random variables describing possible system states, system performance characteristics, and observations. Using big data classification on the simulated scenarios, the probabilities of the system being in a given state, given particular outcomes of observations, may then be straightforwardly evaluated. The application of the presented idea is illustrated through two examples considering damage identification in structural systems subject to extreme loading.
AB - In the field of reliability engineering and systems safety, it is a common challenge to identify the state of a system with basis in a limited set of observations of system performances. Often, there are a multitude of different possible system states, including states of damages, which compete in explaining the observations. To account for these in the context of risk-informed management of systems, the probabilities of the relevant possible different states are needed. In the present contribution, an idea on how this might be supported through big data techniques is presented. Here, systems are considered more holistically and not only as the relationship between input and output. The starting point is to establish a knowledge-consistent probabilistic representation of the system, its key performance characteristics, and the observations (exposures, condition state and performances) that may be collected from the system in reality. Monte Carlo simulations are then employed to establish the relevant scenarios of realizations of the random variables describing possible system states, system performance characteristics, and observations. Using big data classification on the simulated scenarios, the probabilities of the system being in a given state, given particular outcomes of observations, may then be straightforwardly evaluated. The application of the presented idea is illustrated through two examples considering damage identification in structural systems subject to extreme loading.
KW - Big data
KW - Observations
KW - Structural damage identification
KW - System identification
KW - Systems modeling
KW - Big data
KW - Observations
KW - Structural damage identification
KW - System identification
KW - Systems modeling
UR - http://www.scopus.com/inward/record.url?scp=85121470135&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2021.108219
DO - 10.1016/j.ress.2021.108219
M3 - Journal article
AN - SCOPUS:85121470135
SN - 0951-8320
VL - 220
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 108219
ER -