An algorithm for the computationally efficient deductive implementation of the Markov/Cell-to-Cell-Mapping Technique for risk significant scenario identification

Yang, Jun; Aldemir, Tunc

doi:10.1016/j.ress.2015.08.013

Cited by 22 publications

(9 citation statements)

References 24 publications

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Section: Overview and Assumptionsmentioning

confidence: 99%

“…Markov/CCMT is a logic tool used to provide quantified metrics for system reliability and safety [39,5,3,9,8,2,4,13]. Theoretical basis of BPA is presented in the work of Yang and Aldemir [39]. System evolution in time is represented through a series of discrete-time transitions among computational cells that partition the system state-space in a manner similar to finite element or finite difference methods.…”

Section: Overview and Assumptionsmentioning

confidence: 99%

“…In this paper, a generic Backtracking Process Algorithm (BPA) algorithm is proposed for the determination of quantitative metrics that probabilistically rank scenarios leading to user specified Top Events (e.g. hazardous events) by risk significance [39]. Within the context of this paper, a risk significant scenario is defined as a sequence of events that lead to an undesirable consequence with probability higher than a user-specified threshold.…”

Section: Introductionmentioning

confidence: 99%

“…Probabilistic system evolution is quantified in time, and fault propagation is traced throughout the system. Markov/CCMT has mostly been used in literature for the failure analysis and diagnostics of process control systems under uncertainties [2,3,4,5,9,8,13,39]. More specifically, BPA is proposed to solve the problem of tracing fault propagation in systems with complex dynamics and varying configuration, such as random hardware failures of system components.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identification of Risk Significant Automotive Scenarios Under Hardware Failures

Hejase

Kurt

Aldemir

et al. 2018

Electron. Proc. Theor. Comput. Sci.

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The level of autonomous functions in vehicular control systems has been on a steady rise. This rise makes it more challenging for control system engineers to ensure a high level of safety, especially against unexpected failures such as stochastic hardware failures. A generic Backtracking Process Algorithm (BPA) based on a deductive implementation of the Markov/Cell-to-Cell Mapping technique is proposed for the identification of critical scenarios leading to the violation of safety goals. A discretized state-space representation of the system allows tracing of fault propagation throughout the system, and the quantification of probabilistic system evolution in time. A case study of a Hybrid State Control System for an autonomous vehicle prone to a brake-by-wire failure is constructed. The hazard of interest is collision with a stationary vehicle. The BPA is implemented to identify the risk significant scenarios leading to the hazard of interest.

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Section: Overview and Assumptionsmentioning

confidence: 99%

Section: Overview and Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Risk Significant Automotive Scenarios Under Hardware Failures

Hejase

Kurt

Aldemir

et al. 2018

Electron. Proc. Theor. Comput. Sci.

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“…The risk of the fault propagation is estimated by an ant colony algorithm. Yang and Aldemir have proposed a computationally efficient approach based on Markov/cell-to-cell mapping technique for tracing the fault propagation throughout the system [20]. A pruning algorithm is developed to control the complexity reduction rate.…”

Section: Introductionmentioning

confidence: 99%

On fault propagation in deterioration of multi-component systems

Liang¹,

Parlikad²,

Srinivasan³

et al. 2017

Reliability Engineering & System Safety

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In extant literature, deterioration dependence among components can be modelled as inherent dependence and induced dependence. We find that the two types of dependence may co-exist and interact with each other in one multi-component system. We refer to this phenomenon as fault propagation. In practice, a fault induced by the malfunction of a non-critical component may further propagate through the dependence amongst critical components. Such fault propagation scenario happens in industrial assets or systems (bridge deck, and heat exchanging system). In this paper, a multi-layered vector-valued continuous-time Markov chain is developed to capture the characteristics of fault propagation. To obtain the mathematical tractability, we derive a partitioning rule to aggregate states with the same characteristics while keeping the overall aging behaviour of the multi-component system. Although the detailed information of components is masked by aggregated states, lumpability is attainable with the partitioning rule. It means that the aggregated process is stochastically equivalent to the original one and retains the Markov property. We apply this model on a heat exchanging system in oil refinery company. The results show that fault propagation has a more significant impact on the system's lifetime comparing with inherent dependence and induced dependence.

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A hybrid computing framework for risk‐oriented reliability analysis in dynamic PSA context: A case study

Jiang

et al. 2022

Quality & Reliability Eng

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Nowadays, the emerging digital technologies and digitalization trend in safety‐critical industrial process systems are bringing great opportunities for system performance improvements. However, new big challenges are also encountered in the reliability and safety evaluation of large complex industrial process systems due to its multi‐state, multi‐phase dynamic interactions, resilience on software and inter‐dependencies among digital components. The objectives of this study are i) to present an introductory overview of a hybrid computing framework as a supplementary to conventional fault tree analysis toolkit for risk‐oriented reliability analysis in dynamic probabilistic safety assessment context; ii) to illustrate how to combine the three methods of DDET, Markov/CCMT, and GO‐FLOW for integrated risk solutions by a case study of small‐break LOCA in nuclear power plants. Within the hybrid computing framework, the DDET model is implemented based on graph‐based search and sequence diagram refactoring by linking with Markov/CCMT and GO‐FLOW solver for branch probability estimation. The dynamic event tree model is adopted to represent the accident sequence of small‐break LOCA, where the heading events of system failure of digital RPS and phased‐mission ECCS in realization of safety‐critical functions of reactivity control and emergency core cooling are respectively modeled and analyzed by Markov chain and GO‐FLOW method. The demonstration results show that the failure analysis of complex dynamic process interactions together with time‐dependent mission reliability analysis of safety systems involved in accident prevention and mitigation can be easily implemented with accurate modeling and fast evaluation within the hybrid integration platform. The core algorithms and principles implemented for dynamic risk scenarios development by DDET, modeling, and analysis of dynamic process interactions by Markov/CCMT, time‐dependent and multi‐phase mission reliability analysis by GO‐FLOW as well as their integration to provide comprehensive solutions for the application of dynamic reliability in risk assessment are also discussed as open problems for future research.

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An algorithm for the computationally efficient deductive implementation of the Markov/Cell-to-Cell-Mapping Technique for risk significant scenario identification

Cited by 22 publications

References 24 publications

Identification of Risk Significant Automotive Scenarios Under Hardware Failures

Identification of Risk Significant Automotive Scenarios Under Hardware Failures

On fault propagation in deterioration of multi-component systems

A hybrid computing framework for risk‐oriented reliability analysis in dynamic PSA context: A case study

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