Model-based dependability analysis

Sharvia, Septavera; Kabir, Sohag; Walker, Martin; Papadopoulos, Yiannis

doi:10.1016/b978-0-12-802301-3.00012-0

Cited by 36 publications

(11 citation statements)

References 40 publications

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“…Another issue worth mentioning is that even where software tool support exists, it requires a lot of manual effort to create and evaluate different safety and reliability analysis artefacts such as fault trees and FMEAs. Modelbased safety analysis (MBSA) [5,203], which automates much of the generation and evaluation process of safety and reliability artefacts, has attracted significant interest from industry and academia. Although the issue of uncertainty in the failure data has been addressed in classical risk assessment approaches by incorporating fuzzy set theory, no effort has been made to address the same issue in the context of MBSA.…”

Section: Discussion and Future Outlookmentioning

confidence: 99%

A review of applications of fuzzy sets to safety and reliability engineering

Kabir

Papadopoulos

2018

International Journal of Approximate Reasoning

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142

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Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering.

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Section: Discussion and Future Outlookmentioning

confidence: 99%

A review of applications of fuzzy sets to safety and reliability engineering

Kabir

Papadopoulos

2018

International Journal of Approximate Reasoning

Self Cite

142

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“…This has led to the emergence of the field of model-based dependability assessment (MBDA). While certain techniques focus on making the analysis process more manageable, other MBDA techniques have been developed to address the limitations of traditional techniques [39]. The field of MBDA encompasses a large variety of techniques, such as HiP-HOPS workbench [40], FPTN [41], FPTC [42], SAML [43], smartIflow [44], AltaRica [45], and Figaro [46].…”

Section: Related Workmentioning

confidence: 99%

“…Creation of a dedicated dependability model requires more work, but it allows using the optimal level of abstraction and inclusion of only the needed details from reliability and risk analysis point of views. The general underlying formalism and the types of analyses performed typically gravitate MBDA techniques towards two leading paradigms [39]. In failure logic synthesis and analysis (FLSA) the fault tree or other failure model is automatically constructed from the information stored in the system model.…”

Section: Related Workmentioning

confidence: 99%

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An open modelling approach for availability and reliability of systems

Penttinen

Niemi

Gutleber

et al. 2019

Reliability Engineering & System Safety

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This paper introduces an Open Modelling approach for Availability and Reliability of Systems (OpenMARS), which is developed for risk and performance assessment of large and complex systems with dynamic behaviours. The approach allows for combining the most common risk assessment and operation modelling techniques. This ensures a high degree of freedom for the modeller to accurately describe the system without limitations imposed by an individual technique. OpenMARS uses a platform-independent tabular format to define the used modelling technique, to create the model structure, and to assign the parameter values. We developed the format to enable a straightforward manual model definition while maintaining database compatibility. This paper also presents our calculation engine for stochastic simulation-based analysis of OpenMARS models. Our intention is to use this approach as a basis for new software. We demonstrate the feasibility of OpenMARS with an example of a multi-state production process that is subject to failures. The example creates a comprehensive system model by combining interconnected failure logic, operation phase, and production function models. We believe that the advanced features of OpenMARS have wide ranging applications for analysis of reliability, performance, and energy efficiency of complex industrial processes.

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“…FPTC [8] is a further development based on FPTN, in which system model is linked with failure model and requires a repeated analysis after injection failure. HIP-HOPS [9,10] is a hierarchical and modular analysis method, it can be used for system reliability modeling and analysis. In this method, the input failure and output failure are linked, and the SAM tool is used to generate the fault tree and calculate the minimum cut to find all propagation paths that cause the output faults.…”

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confidence: 99%

An Improved FFIP Method Based on Mathematical Logic and SysML

et al. 2021

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In recent years, the model-based safety analysis (MBSA) has been developing continuously. The Functional Failure Identification and Propagation (FFIP) method is a graphics processing technology which supports the analysis of fault propagation paths before making costly design commitments. However, the traditional FFIP has some deficiencies. In this paper, we extend the functional failure logic (FFL) in the FFIP and introduce the concept of deviation. So, FFIP can be used to analyze the failure process of the systems and make the logical analysis of functional failure easier. Based on the extended FFL, we present a new overview of the FFIP. The FFIP is improved by using mathematical logic and Systems Modeling Language (SysML). The standard expression of FFL is realized, which is conducive to the subsequent modeling and modification. Additionally, we use the failure logic analysis in the FFIP to improve the state machine diagram (SMD) in SysML. Finally, the improved FFIP method is used to analyze the fault propagation paths of the system and Simulink is used for simulation. The fault tree is generated according to the simulation results, the minimum cut set is calculated, and the key failure parts of the system are obtained.

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Model-based dependability analysis

Cited by 36 publications

References 40 publications

A review of applications of fuzzy sets to safety and reliability engineering

A review of applications of fuzzy sets to safety and reliability engineering

An open modelling approach for availability and reliability of systems

An Improved FFIP Method Based on Mathematical Logic and SysML

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