Probabilistic Model-Checking Support for FMEA

Hickey, D.

doi:10.1109/qest.2007.18

Cited by 51 publications

(33 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Failure modes and effects criticality analysis (FMECA) is an extension to FMEA that ranks the identified failure modes based on their severity, which is used for prioritization of countermeasures (Becker and Flick 1996;MIL-STD-1629A 1980. Another extension is provided by Grunske et al (2007) who introduced an extension to the conventional FMEA, namely "the probabilistic FMEA." It has the advantage of formally including rates at which component failures can occur.…”

Section: Fmeamentioning

confidence: 99%

Comparison of the FMEA and STPA safety analysis methods–a case study

et al. 2017

View full text Add to dashboard Cite

As our society becomes more and more dependent on IT systems, failures of these systems can harm more and more people and organizations. Diligently performing risk and hazard analysis helps to minimize the potential harm of IT system failures on the society and increases the probability of their undisturbed operation. Risk and hazard analysis is an important activity for the development and operation of critical software intensive systems, but the increased complexity and size puts additional requirements on the effectiveness of risk and hazard analysis methods. This paper presents a qualitative comparison of two hazard analysis methods, failure mode and effect analysis (FMEA) and system theoretic process analysis (STPA), using case study research methodology. Both methods have been applied on the same forward collision avoidance system to compare the effectiveness of the methods and to investigate what are the main differences between them. Furthermore, this study also evaluates the analysis process of both methods by using a qualitative criteria derived from the technology acceptance model (TAM). The results of the FMEA analysis were compared to the results of the STPA analysis, which were presented in a previous study. Both analyses were conducted on the same forward collision avoidance system. The comparison shows that FMEA and STPA deliver similar analysis results.

show abstract

Section: Fmeamentioning

confidence: 99%

Comparison of the FMEA and STPA safety analysis methods–a case study

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Access Complexity and Authentication of the CVSS base score are measured.  FMEA (Failure Mode and Effects Analysis) [17] tool identifies threat vectors, severity of threats, causes and current inspection methodology to evaluate the risks. Here, the Confidential Impact, Integrity Impact and Availability Impact of the CVSS base score are measured.…”

Section: σ-Measure Phase For Security Policymentioning

confidence: 99%

Security Policy Management Process within Six Sigma Framework

Anand¹,

Saniie²,

Oruklu³

2012

JIS

View full text Add to dashboard Cite

This paper presents a management process for creating adaptive, real-time security policies within the Six Sigma 6σ framework. A key challenge for the creation of a management process is the integration with models of known Industrial processes. One of the most used industrial process models is Six Sigma which is a business management model wherein customer centric needs are put in perspective with business data to create an efficient system. The security policy creation and management process proposed in this paper is based on the Six Sigma model and presents a method to adapt security goals and risk management of a computing service. By formalizing a security policy management process within an industrial process model, the adaptability of this model to existing industrial tools is seamless and offers a clear risk based policy decision framework. In particular, this paper presents the necessary tools and procedures to map Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) methodology to security policy management.

show abstract

“…The body of a module is effectively created by Rule 5. After that, basic declaration instructions (Rules 6-8), commands (Rules 9-11) and repair transitions (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22) are created. To complete the translation strategy, formula expressions are created (Rules 23-28) using a set of rules that decomposes all logic expressions (Rules 29-35).…”

Section: Translation Rulesmentioning

confidence: 99%

Systematic Model-Based Safety Assessment Via Probabilistic Model Checking

Gomes

Mota

Sampaio

et al. 2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Safety assessment is a well-established process for assuring the safety and reliability of critical (aeronautical) systems. It uses probabilistic (quantitative) analysis to provide precise measures about the safety requirements of a system. Traditionally, quantitative safety assessment uses fault-tree analysis, but certification authorities also allow the use of Markov models. In this paper we propose a strategy for quantitative safety assessment based on the Prism model-checker. Prism models are extracted systematically from a high-level model via the application of translation rules. We illustrate our strategy with a representative system design from the airborne industry.

show abstract

Probabilistic Model-Checking Support for FMEA

Cited by 51 publications

References 39 publications

Comparison of the FMEA and STPA safety analysis methods–a case study

Comparison of the FMEA and STPA safety analysis methods–a case study

Security Policy Management Process within Six Sigma Framework

Systematic Model-Based Safety Assessment Via Probabilistic Model Checking

Contact Info

Product

Resources

About