The Probability of Dying in a Plane Crash or Having a Safe Flight

Jóźwiak, Ireneusz J.; Marianski, Aleksander; Ćwik, Michał

doi:10.3846/16487788.2015.1015287

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Many engineering systems have such characters as high technology, high investment and high risk. During the operation process, the complex system is enormously affected by the internal and external loads, such as vibration, fatigue, temperature and human operation factors, even subjected to various hazards including natural disasters, terrorist and criminal acts [1][2][3]. Once an accident occurs, it will cause a series of problems such as transport accidents, explosions, casualties, fires and chemical and biological emission.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Markov Model for Risk and Reliability Prediction of Engineering Systems: A Case Study of a Subsea Wellhead Connector

et al. 2020

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In production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Markov method, this paper proposed a fuzzy Markov method that takes the full advantages of the three methods and makes the analysis of risk, reliability, availability and uncertainty all in one. This method uses the fault tree and fuzzy theory to preprocess the input failure data to improve the reliability of the input failure data, and then input the preprocessed failure data into the Markov model; after that iterate and adjust the model when uncertainty events occur, until the data of all events have been processed by the model and the updated model obtained, which best reflects the system state. The wellhead connector of a subsea production system was used as a case study to demonstrate the above method. The obtained reliability index (mean time to failure) of the connector is basically consistent with the failure statistical data from the offshore and onshore reliability database, which verified the accuracy of the proposed method.

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Section: Introductionmentioning

confidence: 99%