A Benchmark System for Comparing Reliability Modeling Approaches for Digital Instrumentation and Control Systems

Kirschenbaum, Jason; Bucci, Paolo; Stovsky, Michael P.; Mandelli, Diego; Aldemir, Tunc; Yau, Michael; Guarro, Sergio; Ekici, Eylem; Arndt, Steven A.

doi:10.13182/nt09-a4062

Cited by 23 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arguably, PRA has been "very much hardware oriented" [Mosleh, 2014]. A gap exists in current methods to account for software failures or contributions to accidents [Leveson, 1995; and model them with tools that are compatible with traditional PRA [Apostolakis, 2004;Kirschenbaum et al, 2009]. The need to leverage new tools and perspectives for software safety analysis has been argued by several authors [DOD, 2012;Zio, 2014;Mosleh, 2014].…”

Section: Limitations Of Traditional Pra and Proposed Improvementsmentioning

confidence: 99%

“…The transition probabilities among the normal and abnormal configurations (or a more complex model of the stochastic rules that govern each transition). Kirschenbaum et al [2009], Aldemir [2013], and Zio [2014] provide a survey of different DPRA methodologies including dynamic flowgraph methods, Markov/cell-to-cell mapping techniques, and Petri nets. These techniques have the potential to uncover and identify plant vulnerabilities that were apriori unknown, and that could not be considered with traditional PRA tools.…”

Section: Dpra: An Answer To the Time-dependency Limitations Of Traditmentioning

confidence: 99%

“…Benchmark examples continue to be developed for the consistent comparison of the different risk assessment methodologies [Kirschenbaum et al, 2009;Aldemir et al, 2010].…”

Section: Dpra: An Answer To the Time-dependency Limitations Of Traditmentioning

confidence: 99%

See 2 more Smart Citations

Toward risk assessment 2.0: Safety supervisory control and model-based hazard monitoring for risk-informed safety interventions

Favaró

Saleh

2016

Reliability Engineering & System Safety

View full text Add to dashboard Cite

Probabilistic Risk Assessment (PRA) is a staple in the engineering risk community, and it has become to some extent synonymous with the entire quantitative risk assessment undertaking. Limitations of PRA continue to occupy researchers, and workarounds are often proposed. After a brief review of this literature, we propose to address some of PRA's limitations by developing a novel framework and analytical tools for model-based system safety, or safety supervisory control, to guide safety interventions and support a dynamic approach to risk assessment and accident prevention. Our work shifts the emphasis from the pervading probabilistic mindset in risk assessment toward the notions of danger indices and hazard temporal contingency. The framework and tools here developed are grounded in Control Theory and make use of the state-space formalism in modeling dynamical systems. We show that the use of state variables enables the definition of metrics for accident escalation, termed hazard levels or danger indices, which measure the "proximity" of the system state to adverse events, and we illustrate the development of such indices. Monitoring of the hazard levels provides diagnostic information to support both online and offline safety interventions. For example, we show how the application of the proposed tools to a rejected takeoff scenario provides new insight to support pilots' go/no-go decisions. Furthermore, we augment the traditional state-space equations with a hazard equation and use the latter to estimate the times at which critical thresholds for the hazard level are (b)reached. This estimation process provides important prognostic information and produces a proxy for a time-to-accident metric or advance notice for an impending adverse event. The ability to estimate these two hazard coordinates, danger index and time-to-accident, offers many possibilities for informing system control strategies and improving accident prevention and risk mitigation. Finally we develop a visualization tool, termed hazard temporal contingency map, which dynamically displays the "coordinates" of a portfolio of hazards. This tool is meant to support operators' situational awareness by providing prognostic information regarding the time windows available to intervene before hazardous situations become unrecoverable, and it helps decision-makers prioritize attention and defensive resources for accident prevention. In this view, emerging risks and hazards are dynamically prioritized based on the temporal vicinity of their associated accident(s) to being released, not on probabilities or combination of probabilities and consequences, as is traditionally done (offline) in PRA.This approach offers novel capabilities, complementary to PRA, for improving risk assessment and accident prevention. It is hoped that this work helps to expand the basis of risk assessment beyond its reliance on probabilistic tools, and that it serves to enrich the intellectual toolkit of risk researchers and safety professionals.

show abstract

Section: Limitations Of Traditional Pra and Proposed Improvementsmentioning

confidence: 99%

Section: Dpra: An Answer To the Time-dependency Limitations Of Traditmentioning

confidence: 99%

See 1 more Smart Citation

Toward risk assessment 2.0: Safety supervisory control and model-based hazard monitoring for risk-informed safety interventions

Favaró

Saleh

2016

Reliability Engineering & System Safety

View full text Add to dashboard Cite

show abstract

“…Such a comparison was performed in Ref. 25, which presents several scenarios based upon Licensee Event Reports to demonstrate the ability of the benchmark system to meet LCC features 2, 4, 7, 8 and 9. These features, respectively, include the representation of power, device polling, interaction with the plant process, a self-diagnostic system, and the use of watchdog timers.…”

Section: Iiib2 Comparison Of Benchmark System With Desirable Featumentioning

confidence: 99%

A Benchmark System for Comparing Reliability Modeling Approaches for Digital Instrumentation and Control Systems

2009

Nucl Technol

View full text Add to dashboard Cite

“…However, FTs and ETs cannot adequately account for the impact of the dynamic interactions among continuous physical parameters of the process (temperature, pressure, speed, etc. ), stochastic discrete failure events of the hardware and software components, and human operator actions dynamic systems . PRA of dynamic systems also calls for accounting for sequence and timing of the events because these influence the development of the accident scenario, and minimal cut sets (MCS), i.e., the minimal combination of elementary events that make the whole system fail within a static reliability analysis, lose their meaning .…”

Section: Introductionmentioning

confidence: 99%

A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems

2014

View full text Add to dashboard Cite

Dynamic reliability methods aim at complementing the capability of traditional static approaches (e.g., event trees [ETs] and fault trees [FTs]) by accounting for the system dynamic behavior and its interactions with the system state transition process. For this, the system dynamics is here described by a time-dependent model that includes the dependencies with the stochastic transition events. In this article, we present a novel computational framework for dynamic reliability analysis whose objectives are i) accounting for discrete stochastic transition events and ii) identifying the prime implicants (PIs) of the dynamic system. The framework entails adopting a multiple-valued logic (MVL) to consider stochastic transitions at discretized times. Then, PIs are originally identified by a differential evolution (DE) algorithm that looks for the optimal MVL solution of a covering problem formulated for MVL accident scenarios. For testing the feasibility of the framework, a dynamic noncoherent system composed of five components that can fail at discretized times has been analyzed, showing the applicability of the framework to practical cases.

show abstract

A Benchmark System for Comparing Reliability Modeling Approaches for Digital Instrumentation and Control Systems

Cited by 23 publications

References 16 publications

Toward risk assessment 2.0: Safety supervisory control and model-based hazard monitoring for risk-informed safety interventions

Toward risk assessment 2.0: Safety supervisory control and model-based hazard monitoring for risk-informed safety interventions

A Benchmark System for Comparing Reliability Modeling Approaches for Digital Instrumentation and Control Systems

A Computational Framework for Prime Implicants Identification in Noncoherent Dynamic Systems

Contact Info

Product

Resources

About