Details
| Original language | English |
|---|---|
| Article number | 110309 |
| Number of pages | 18 |
| Journal | Reliability Engineering and System Safety |
| Volume | 250 |
| Early online date | 27 Jun 2024 |
| Publication status | E-pub ahead of print - 27 Jun 2024 |
Abstract
Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample component lifetimes and the K-terminal spanning tree algorithm to accelerate structure function computation. Unlike existing methods that compute only one structure function value per sample, our method generates multiple component state vectors and corresponding structure function values from each sample. Network reliability is estimated based on survival signatures derived from these values. A transformation technique extends this method to handle both node failure and edge failure. To enhance efficiency of proposed sampling method and achieve adaptability to network topology changes, we introduce an active learning method utilizing a random forest (RF) classifier. This classifier directly predicts structure function values, integrates network behaviors across diverse topologies, and undergoes iterative refinement to enhance predictive accuracy. Importantly, the trained RF classifier can directly predict reliability for variant networks, a capability beyond the sampling method alone. Through investigating several network examples and two practical applications, the effectiveness of both proposed methods is demonstrated.
Keywords
- Active learning, K-terminal spanning tree, Monte Carlo simulation, Network reliability, Random forest classification, Survival signature
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Reliability Engineering and System Safety, Vol. 250, 110309, 10.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Sampling and active learning methods for network reliability estimation using K-terminal spanning tree
AU - Ding, Chen
AU - Wei, Pengfei
AU - Shi, Yan
AU - Liu, Jinxing
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Publisher Copyright: © 2024 Elsevier Ltd
PY - 2024/6/27
Y1 - 2024/6/27
N2 - Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample component lifetimes and the K-terminal spanning tree algorithm to accelerate structure function computation. Unlike existing methods that compute only one structure function value per sample, our method generates multiple component state vectors and corresponding structure function values from each sample. Network reliability is estimated based on survival signatures derived from these values. A transformation technique extends this method to handle both node failure and edge failure. To enhance efficiency of proposed sampling method and achieve adaptability to network topology changes, we introduce an active learning method utilizing a random forest (RF) classifier. This classifier directly predicts structure function values, integrates network behaviors across diverse topologies, and undergoes iterative refinement to enhance predictive accuracy. Importantly, the trained RF classifier can directly predict reliability for variant networks, a capability beyond the sampling method alone. Through investigating several network examples and two practical applications, the effectiveness of both proposed methods is demonstrated.
AB - Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample component lifetimes and the K-terminal spanning tree algorithm to accelerate structure function computation. Unlike existing methods that compute only one structure function value per sample, our method generates multiple component state vectors and corresponding structure function values from each sample. Network reliability is estimated based on survival signatures derived from these values. A transformation technique extends this method to handle both node failure and edge failure. To enhance efficiency of proposed sampling method and achieve adaptability to network topology changes, we introduce an active learning method utilizing a random forest (RF) classifier. This classifier directly predicts structure function values, integrates network behaviors across diverse topologies, and undergoes iterative refinement to enhance predictive accuracy. Importantly, the trained RF classifier can directly predict reliability for variant networks, a capability beyond the sampling method alone. Through investigating several network examples and two practical applications, the effectiveness of both proposed methods is demonstrated.
KW - Active learning
KW - K-terminal spanning tree
KW - Monte Carlo simulation
KW - Network reliability
KW - Random forest classification
KW - Survival signature
UR - http://www.scopus.com/inward/record.url?scp=85197249605&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2024.110309
DO - 10.1016/j.ress.2024.110309
M3 - Article
AN - SCOPUS:85197249605
VL - 250
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 110309
ER -