A new approach to common cause failure

Hughes, R. P.

doi:10.1016/0143-8174(87)90044-8

Cited by 58 publications

(30 citation statements)

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“…We also note that these bounding methods also apply to the models by Hughes [6] that explain correlation between random failures in redundant hardware in terms of variation of failure rates with environmental stress. Variation of failure rates with environmental factors will typically be easier to assess than variation of difficulty between demands.…”

Section: Discussionmentioning

confidence: 99%

Estimating Worst Case Failure Dependency with Partial Knowledge of the Difficulty Function

Bishop

Strigini

2014

Lecture Notes in Computer Science

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-For systems using software diversity, well-established theories show that the expected probability of failure on demand (pfd) for two diverse program versions failing together will generally differ from what it would be if they failed independently. This is explained in terms of a "difficulty function" that varies between demands on the system. This theory gives insight, but no specific prediction unless we have some means to quantify the difficulty function. This paper presents a theory leading to a worst case measure of "average failure dependency" between diverse software, given only partial knowledge of the difficulty function. It also discusses the possibility of estimating the model parameters, with one approach based on an empirical analysis of previous systems implemented as logic networks, to support pre-development estimates of expected gain from diversity. The approach is illustrated using a realistic safety system example. Permanent repository link

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Section: Discussionmentioning

confidence: 99%

Estimating Worst Case Failure Dependency with Partial Knowledge of the Difficulty Function

Bishop

Strigini

2014

Lecture Notes in Computer Science

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“…Common stresses (e.g. like elevated temperatures) or shocks are modeled as creating specific demand classes where hardware pfds are increased, but still failures are independent conditional on this higher pfd (possibly very close to 1, for shocks affecting hardware in both trains) [5].…”

Section: Figure 1 Independent Trains (Hardware Based)mentioning

confidence: 99%

“…This form of modeling has been used in earlier work [4][5][6] to model in a consistent way failures due both to physical causes and to design, and thus both hardware and software failures. The basic idea here is one of variation of the probability of system failure between different demands, as an explanation for dependence in failure behavior between diverse trains to be used in a fault tolerant system (e.g.…”

Section: Introductionmentioning

confidence: 99%

A conservative bound for the probability of failure of a 1-out-of-2 protection system with one hardware-only and one software-based protection train

Bishop

Bloomfield

Littlewood

et al. 2014

Reliability Engineering & System Safety

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“…However, it has been keenly observed that human error and common-cause failure are very much influencing constraints in performance and/or reliability analysis of system repair problems. Few earlier research workers confined the reliability behavior of k-out of-n systems taking into account the common-cause failure, for example, Hughes (1987) presented a new approach to common-cause failure in analyzing system repair problems and Who Kee Chang (1987) attempted for reliability analysis of a repairable parallel system with standby involving human failure and common-cause failures yet no attention has been paid to the performance evaluation of k-out of-n repairable system due to human error and system failure constraints. Jain et al (2002) studied a k-out of-n system with dependent failure and standby support and Moustafa (1997) also studied k-out of-n repairable system with dependent failure and imperfect coverage.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling for Performance Analysis and Inference of k-out of-n Repairable System Integrating Human Error and System Failure

Maurya¹

2014

AJMO

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Present paper demonstrates mathematical modeling and evaluation of performance measures of k-out of-n repairable system wherein the most influencing constraints of human error and common-cause failure have been taken into consideration. Firstly the mathematical modeling is developed for performance analysis of k-out of-n repairable system with standby units involving human and common-cause failure. Then, a successful attempt has been made to evaluate various important performance measures such as availability of system, steady state availability and mean time of system failure (MTSF), mean operational time(MOT), expected busy period (EBP) and steady state busy period etc. Using the supplementary variable technique, Laplace transforms of various state probabilities are explored. Moreover, a particular case when repair rate follows exponential distribution has also been discussed. In addition, numerical illustration has also been presented in order to enable a better mode for understanding and testing the outcomes explored herein. Finally, tables and graphs for investigated results are displayed for drawing some significant conclusive observations for testing their validity and consistency.

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A new approach to common cause failure

Cited by 58 publications

References 1 publication

Estimating Worst Case Failure Dependency with Partial Knowledge of the Difficulty Function

Estimating Worst Case Failure Dependency with Partial Knowledge of the Difficulty Function

A conservative bound for the probability of failure of a 1-out-of-2 protection system with one hardware-only and one software-based protection train

Mathematical Modeling for Performance Analysis and Inference of k-out of-n Repairable System Integrating Human Error and System Failure

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