Extend GO Methodology to Support Common-Cause Failures Modeling Explicitly by Means of Bayesian Networks

Ye, Tianyuan; Zhou, Yuan; Chen, Anran; Liu, Linlin; Liu, Shouwen

doi:10.1109/tr.2019.2917752

Cited by 13 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Markov models are useful for analysis of dynamic properties of the system reliability [16], [17]. Bayesian networks are recommended for the analysis of system reliability based on uncertain initial data [2], [18]. The Universal Generating Function is an effective mathematical representation for calculation of system reliability, which allows mapping relation between the working states of the system or the components and the corresponding state probabilities [19].…”

Section: The Problem State Analysismentioning

confidence: 99%

Logic Differential Calculus for Reliability Analysis Based on Survival Signature

Rusnak¹,

Zaitseva²,

Coolen

et al. 2023

IEEE Trans. Dependable and Secure Comput.

View full text Add to dashboard Cite

Section: The Problem State Analysismentioning

confidence: 99%

Logic Differential Calculus for Reliability Analysis Based on Survival Signature

Rusnak¹,

Zaitseva²,

Coolen

et al. 2023

IEEE Trans. Dependable and Secure Comput.

View full text Add to dashboard Cite

“…There are several methods to evaluate the availability of a system, among which Bayesian networks have gained large acceptance within the industry and research 41–44 …”

Section: Related Workmentioning

confidence: 99%

“…There are several methods to evaluate the availability of a system, among which Bayesian networks have gained large acceptance within the industry and research. [41][42][43][44] Bobbio et al 28,45 demonstrated the applicability and superiority of Bayesian networks in modeling and evaluating equivalent fault trees. 26 Moreover, Boudali and Dugan 35,36 showed how to use dynamic Bayesian networks to model dynamic fault trees as well, effectively proving that the Bayesian network formalism is powerful enough to cover all non-state space models.…”

Section: Related Workmentioning

confidence: 99%

Availability analysis of redundant and replicated cloud services with Bayesian networks

Bibartiu

Dürr

Rothermel

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

Due to the growing complexity of modern data centers, failures are not uncommon any more. Therefore, fault tolerance mechanisms play a vital role in fulfilling the availability requirements. Multiple availability models have been proposed to assess compute systems, among which Bayesian network models have gained popularity in industry and research due to its powerful modeling formalism. In particular, this work focuses on assessing the availability of redundant and replicated cloud computing services with Bayesian networks. So far, research on availability has only focused on modeling either infrastructure or communication failures in Bayesian networks, but have not considered both simultaneously. This work addresses practical modeling challenges of assessing the availability of large-scale redundant and replicated services with Bayesian networks, including cascading and common-cause failures from the surrounding infrastructure and communication network. In order to ease the modeling task, this paper introduces a high-level modeling formalism to build such a Bayesian network automatically. Performance evaluations demonstrate the feasibility of the presented Bayesian network approach to assess the availability of largescale redundant and replicated services. This model is not only applicable in the domain of cloud computing it can also be applied for general cases of local and geo-distributed systems.

show abstract

“…The fault propagation mechanism-based method usually offers advantages of intuitive presentation, easy management, reusability, and migration. There are many techniques, such as Fault Tree Analysis (FTA), 2 Failure Mode and Effect Analysis (FMEA), 3 Bayesian Network (BN), [4][5][6] Dynamic Uncertainty Causality Graph (DUCG), [7][8][9] Petri Net (PN), 10,11 GO method, 5 F2G method, 12 etc., that are widely employed in engineering and research fields with the limited real-time operation data. In the last few decades, FMEA has typically been used to analyse and provide fault information, including failure mode and effect, for fault propagation mechanisms-based methods.…”

Section: Introductionmentioning

confidence: 99%

Physics‐data‐fusion based decoupling model for coupled faults of complex electromechanical systems

Wang

Liang

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

Coupled faults are formed by the nonlinear coupling of multiple lower-level faults in complex electromechanical systems (CES). Although fault decoupling plays a crucial role in locating fault cause and isolating fault components, it still faces challenges due to the harsh reality of common mode failure, networked propagation, and a lack of accurate fault mechanism knowledge in the fault coupling process. A novel physics-data-fusion-based decoupling model for coupled faults of CES was proposed using standard meta components, rigorous formulation, and intuitive representation. First, a hierarchical graph representing the static complex decoupling model was defined by composing proposed meta models. Second, the dynamic model parameters inspired by the time-varying fault characteristics were determined using real-time operation data analysis. Then, based on a proposed numerical reasoning formula, the most likely fault cause was determined, which can also identify fault level by level. Finally, the decoupling model was proved to be reasonable and effective with an offshore wind turbine case. As a graphical modelling method, it handles the decoupling process by fusing static physics and dynamic data of coupled faults. While inheriting the benefits of conventional models, it overcomes the limitations of these existing methods for real-time results. Moreover, the proposed method provided a foundation for tracing the root cause of performance fluctuations, fault detection, and isolation of CES.

show abstract

Extend GO Methodology to Support Common-Cause Failures Modeling Explicitly by Means of Bayesian Networks

Cited by 13 publications

References 26 publications

Logic Differential Calculus for Reliability Analysis Based on Survival Signature

Logic Differential Calculus for Reliability Analysis Based on Survival Signature

Availability analysis of redundant and replicated cloud services with Bayesian networks

Physics‐data‐fusion based decoupling model for coupled faults of complex electromechanical systems

Contact Info

Product

Resources

About