A stochastic hybrid systems model of common-cause failures of degrading components

Fan, Mengfei; Zeng, Zhiguo; Zio, Enrico; Kang, Rui; Chen, Ying

doi:10.1016/j.ress.2017.12.003

Cited by 37 publications

(13 citation statements)

References 37 publications

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“…The system was first analysed with and without CCF, using the proposed simulation model and the data presented in Table 1. For this system, L = f2, 4, 1, 2, 4g and the CCF matrices, with and without CCF, are as expressed in equations (7) and (8), respectively…”

Section: Case Study 1: a Complex Bridge Systemmentioning

confidence: 99%

“…A robust Bayesian approach for quantifying the α -factor parameters of a CCG in the presence of epistemic uncertainties has also been put forward by Troffaes et al 7 Their approach, however, is limited to component level reliability and, therefore, requires a second approach to obtain the system level reliability indices. For this, Fan et al’s 8 stochastic hybrid systems model, O’Connor and Mosleh’s 9 general cause-based methodology, or Ramirez-Marquez and Coit’s 10 reliability optimisation approach would do. Of course, only if the reliability analyst is willing to turn a blind eye to their respective drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Extending the survival signature paradigm to complex systems with non-repairable dependent failures

George-Williams

Geng

Coolen

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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Dependent failures impose severe consequences on a complex system’s reliability and overall performance, and a realistic assessment, therefore, requires an adequate consideration of these failures. System survival signature opens up a new and efficient way to compute a system’s reliability, given its ability to segregate the structural from the probabilistic attributes of the system. Consequently, it outperforms the well-known system reliability evaluation techniques, when solicited for problems like maintenance optimisation, requiring repetitive system evaluations. The survival signature, however, is premised on the statistical independence between component failure times and, more generally, on the theory of weak exchangeability, for dependent component failures. The assumption of independence is flawed for most realistic engineering systems while the latter entails the painstaking and sometimes impossible task of deriving the joint survival function of the system components. This article, therefore, proposes a novel, generally applicable, and efficient Monte Carlo Simulation approach that allows the survival signature to be intuitively used for the reliability evaluation of systems susceptible to induced failures. Multiple component failure modes, as well, are considered, and sensitivities are analysed to identify the most critical common-cause group to the survivability of the system. Examples demonstrate the superiority of the approach.

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Section: Case Study 1: a Complex Bridge Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extending the survival signature paradigm to complex systems with non-repairable dependent failures

George-Williams

Geng

Coolen

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…The current modeling methods of common-cause failure mainly include the α-factor model [29], [30], the beta factor model [31], the multiple beta factor (MBF) model [32], the multiple Greek letter (MGL) model [33], the basic parameter (BP) model [34], the binomial failure rate (BFR) model [35], and the multiple error shock (MESH) model [36] etc. In addition, Fan et al [37] developed a new model for CCFs considering components degradation based on Stochastic Hybrid System (SHS), the model can accurately describe the CCF effect on the reliability of a system with degrading components. Among them, the β-factor (i.e.…”

Section: Introductionmentioning

confidence: 99%

Reliability and Safety Modelling of the Electrical Control System of the Subsea Control Module Based on Markov and Multiple Beta Factor Model

et al. 2019

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Replacement of the subsea control module (SCM) and halting production caused by the failure of the SCM are costly, and the electrical control system plays a large role in the failure modes of the SCM. Hence, the analysis of its reliability and safety is important. Markov processes and the multiple beta factor model are used to model the reliability and safety for the electrical control system of the SCM that consider the effects of multiple factors, including the failure detection rate, common-cause failure, and the failure rate of each module, for evaluating system reliability and safety. The Morris screening method that is based on a large amount of random data with equal probability is applied to reliability and safety models that vary with time for quantifying the effect levels of each factor on the system reliability and safety in the time interval. The effect of each factor on the system and its interaction degree with other factors are evaluated in the time interval. Finally, the system model is simulated in MATLAB. The simulation demonstrates that the system reliability and safety gradually decrease and the effect of each factor on them increases over time; the reliability is most sensitive to the failure rates of the input, central processing unit, and output modules, whereas the safety is most sensitive to the failure detection rate. The common-cause failure and the comparator module have some effects on the reliability and safety of the system.

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“…Panteli [6,7] discussed the difference between power system resilience and reliability. Reliability research [8,9] concentrates more on small-scale and random faults of power system components caused by internal factors. In contrast, under extreme disaster conditions, the occurrence possibility of wide-range components permanent failures will increase sharply; the common control and restoration measures are difficult to implement, greatly prolonging the duration of outages.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Resilience Assessment under a Tri-Stage Framework for Power Systems

Zhang

Yuan

et al. 2018

Energies

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The frequent occurrence of natural disasters and malicious attacks has exerted unprecedented disturbances on power systems, accounting for the extensive attention paid to power system resilience. Combined with the evolving nature of general disasters, this paper proposes resilience assessment approaches for power systems under a tri-stage framework. The pre-disaster toughness is proposed to quantify the robustness of power systems against potential disasters, where the thinking of area division and partitioned multi-objective risk method (PMRM) is introduced. In the case of information deficiency caused by disasters, the during-disaster resistance to disturbance is calculated to reflect the real-time system running state by state estimation (SE). The post-disaster restoration ability consists of response ability, restoration efficiency and restoration economy, which is evaluated by Sequential Monte-Carlo Simulation to simulate the system restoration process. Further, a synthetic metric system is presented to quantify the resilience performance of power systems from the above three aspects. The proposed approaches and framework are validated on the IEEE RTS 79 system, and helpful conclusions are drawn from extensive case studies.

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A stochastic hybrid systems model of common-cause failures of degrading components

Cited by 37 publications

References 37 publications

Extending the survival signature paradigm to complex systems with non-repairable dependent failures

Extending the survival signature paradigm to complex systems with non-repairable dependent failures

Reliability and Safety Modelling of the Electrical Control System of the Subsea Control Module Based on Markov and Multiple Beta Factor Model

Quantitative Resilience Assessment under a Tri-Stage Framework for Power Systems

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