Dependability Metrics to Assess Safety-Critical Systems

DeLong, T.A.; Smith, D.T.; Johnson, B.W.

doi:10.1109/tr.2005.853567

Cited by 41 publications

(13 citation statements)

References 4 publications

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“…• Mean Time To Unsafe Failure (MTTUF): represents the average time that a system will operate safely before the occurrence of a failure that produces an unsafe system state [50]. MTTUF should be as high as possible, indicating that there are few probable unsafe failures for the whole system, and thus a robust dependable system.…”

Section: Dependability and Safety Metricsmentioning

confidence: 99%

“…MTTUF should be as high as possible, indicating that there are few probable unsafe failures for the whole system, and thus a robust dependable system. • Reliability: as a function of time, or survivor function R(t), is the probability of a system which does not fail in a determined time interval [50]. From R(t) it is possible to compute the reliability of the whole system.…”

Section: Dependability and Safety Metricsmentioning

confidence: 99%

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A three-stage analysis of IDS for critical infrastructures

Cazorla

Alcaraz

López

2015

Computers & Security

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Section: Dependability and Safety Metricsmentioning

confidence: 99%

Section: Dependability and Safety Metricsmentioning

confidence: 99%

A three-stage analysis of IDS for critical infrastructures

Cazorla

Alcaraz

López

2015

Computers & Security

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“…This failure harms the EUC and lead to an accident condition. References [4,[8][9][10] assume, following failure, that a system can exist either in FS or FU state.…”

Section: Introductionmentioning

confidence: 99%

“…Safety indices such as probability of failure on demand 1 (PFD) [1] , mean time to dangerous failure (MTTFD) [3] , mean time to hazardous event (MTTHE) [10] , mean time to unsafe failure (MTTUF) [9] and steady state safety (SSS) [11] etc., aim at the measuring likelihood of system s unsafe failure. A review of different techniques by Rouvroye [12] suggests Markov analysis covers most aspects for quantitative safety evaluation.…”

Section: Introductionmentioning

confidence: 99%

Analyzing effect of demand rate on safety of systems with periodic proof-tests

Kumar

Verma

Srividya

2007

Int J Automat Comput

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Quantitative safety assessment of safety systems plays an important role in decision making at all stages of system lifecycle, i.e., design, deployment and phase out. Most safety assessment methods consider only system parameters, such as configuration, hazard rate, coverage, repair rate, etc. along with periodic proof-tests (or inspection). Not considering demand rate will give a pessimistic safety estimate for an application with low demand rate such as nuclear power plants, chemical plants, etc. In this paper, a basic model of IEC 61508 is used. The basic model is extended to incorporate process demand and behavior of electronic-and/or computer-based system following diagnosis or proof-test. A new safety index, probability of failure on actual demand (PFAD) based on extended model and demand rate is proposed. Periodic proof-test makes the model semi-Markovian, so a piece-wise continuous time Markov chain (CTMC) based method is used to derive mean state probabilities of elementary or aggregated state. Method to determine probability of failure on demand (PFD) (IEC 61508) and PFAD based on these state probabilities are described. In example, safety indices of PFD and PFAD are compared.

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“…This means that the monitoring algorithm plays an important role in FTCS, so it is necessary to establish the reliability model with failure coverage rate and isolate rate. Although reference [4] and [5] established the reliability model considering the monitoring performance and analyzed the influence of fault coverage rate, the redundant degradation and system reconfiguration were not taken into account in reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

Integrated reliability metrics to assess fault tolerant control system

Wang

Tomovic

Shi

2007

2007 IEEE International Conference on Systems, Man and Cybernetics

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This paper presents an integrated reliability definition and mathematical metrics based on function reliability and performance reliability, in which the function reliability highlights failure causality from system to components and the performance reliability focuses on the dynamic performance degradation with operational time. Based on the simulation techniques, this paper establishes the simulation template for typical control system that can describe the dynamic process of performance in continuous time until the parameters exceed the designated threshold. Considering the influence of monitoring voting system, this paper establishes the Markov model with fault detection rate, isolation rate and false alarm rate, investigates the influence to whole system reliability of such factors. Application of triple redundant actuator indicates that the integrated reliability assessment makes full use of the advantages on dynamic fault tree analysis, Markov Model and control performance simulation, and solves the difficulties on calculating reliability considering the characteristics of monitoring system and performance degradation.Index Termsintegrated reliability metrics, dynamic fault tree, Markov model, fault tolerant control system, failure cover rate, false alarm rate

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Dependability Metrics to Assess Safety-Critical Systems

Cited by 41 publications

References 4 publications

A three-stage analysis of IDS for critical infrastructures

A three-stage analysis of IDS for critical infrastructures

Analyzing effect of demand rate on safety of systems with periodic proof-tests

Integrated reliability metrics to assess fault tolerant control system

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