https://ris.utwente.nl/ws/files/13291952/FTA_overview.pdf

Fault tree analysis

The objective of this work is to determine and study, analyze and elaborate, classify and categorize the main risk analysis and risk-assessment methods and techniques by reviewing the scientific literature. The paper consists of two parts: a) the investigation, presentation and elaboration of the main risk-assessment methodologies and b) the statistical analysis, classification, and comparative study of the corresponding scientific papers published by six representative scientific journals of Elsevier B.V. covering the decade 2000–2009. The scientific literature reviewing showed that the risk analysis and assessment techniques are classified into three main categories: (a) the qualitative, (b) the quantitative, and (c) the hybrid techniques (qualitative–quantitative, semi-quantitative). The qualitative techniques are based both on analytical estimation processes, and on the safety managers–engineers ability. According to quantitative techniques, the risk can be considered as a quantity, which can be estimated and expressed by a mathematical relation, under the help of real accidents’ data recorded in a work site. The hybrid techniques, present a great complexity due to their ad hoc character that prevents a wide spreading. The statistical analysis shows that the quantitative methods present the highest relative frequency (65.63%) while the qualitative a lower one (27.68%). Furthermore the hybrid methods remain constantly at a very low level (6.70%) during the entire processing period.

http://risktr.com/dokumanlar/risk%20analysis%20and%20assessment%20methodologies%20in%20th%20work%20sites.pdf

Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000–2009

Methods and models in process safety and risk management: Past, present and future

Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software tools. This paper surveys over 150 papers on fault tree analysis, providing an in-depth overview of the state-of-the-art in FTA. Concretely, we review standard fault trees, as well as extensions such as dynamic FT, repairable FT, and extended FT. For these models, we review both qualitative analysis methods, like cut sets and common cause failures, and quantitative techniques, including a wide variety of stochastic methods to compute failure probabilities. Numerous examples illustrate the various approaches, and tables present a quick overview of results.

Fault Tree Analysis : A survey of the state-of-the-art in modeling, analysis and tools

Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence

Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment

Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies

Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach

Uncertainty is inevitable in any reliability analysis of complex engineering systems due to uncertainties present in models, parameters of the model, phenomena and assumptions. Uncertainties at the component level are propagated to quantify uncertainty at the system level reliability. It is very important to identify all the uncertainties and treat them effectively to make reliability studies more useful for decision making. Conventional probabilistic approaches adopt probability distributions to characterize uncertainty where as fuzzy reliability models adopt membership functions to characterize uncertainty. Both the approaches are widely used in uncertainty propagation for reliability studies. However, identification of critical parameters based on their uncertainty contribution at the system level is very important for effective management of uncertainty. A method is proposed here in the fuzzy framework to rank the components based on their uncertainty contribution to the over all uncertainty of system reliability. It is compared with probabilistic methods using a practical reliability problem in the literature.

A New Uncertainty Importance Measure in Fuzzy Reliability Analysis

Uncertainties are present in any reliability calculations due to randomness in the failure/repair phenomena and given the limitation in assessing the parameters of the failure/repair probability density functions. The simplifications and idealizations also generate uncertainties which can be classified as aleatory (arising due to randomness) and/or epistemic (due to lack of knowledge). It is very important to identify all the uncertainties and treat them effectively to make reliability studies more useful for decision making. The uncertainty propagation of system reliability assessment quantifies uncertainty in system characteristic (e.g. system unavailability) by synthesizing the uncertainties in component characteristics. This paper explores various uncertainty propagation methods used in availability assessment of complex engineering systems. Apart from probabilistic (analytical and sampling) and fuzzy arithmetic approaches, Dempster-Shafer theory based approach is attempted to deal the same problem. A case study on Main Control Power Supply System (MCPS) of typical Indian Nuclear Power Plant (NPP) is presented highlighting merits and demerits of various methods.

K. Durga Rao

Papers

Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment

Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies

Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach

A New Uncertainty Importance Measure in Fuzzy Reliability Analysis

Epistemic Uncertainty Propagation in Reliability Assessment of Complex Systems