Wyniki wyszukiwania - BazTech

1

Interval-valued intuitionistic fuzzy failure modes and effect analysis of the system failure risk estimation

Nguyen H.

Journal of KONES

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2017

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Vol. 24, No. 2

159--166

EN

Among the risk assessment methods, failure modes and effects analysis (FMEA) is a popular, widely used engineering technique in many areas. It can be used to identify and eliminate known or potential failure modes to enhance reliability and safety of complex systems. In practice, risk estimations encounter difficulties connected with shortage of data. In such cases, we have to rely on subjective estimations made by persons with practical knowledge in the field of interest, i.e. experts. However, in some realistic situations, the decision makers might be unable to assign the exact values to the evaluation judgments due to his/her limited knowledge. In other words, there is a certain degree of hesitancy in human cognition and his/her judgment, who may have insufficient knowledge of the problem domain or uncertainty in assigning the evaluation values to the objects considered. In order to deal with ambiguity and uncertainty in the imperfect information, there have been recently proposed many various such theories as fuzzy sets, interval-valued fuzzy sets, type-2 fuzzy sets, hesitant sets, grey sets, rough sets and intuitionistic fuzzy sets. They have drawn more and more attention of scholars and been adopted in many applications This article addresses the Atanassov’s interval-valued intuitionistic fuzzy sets and FMEA methods in the risk estimation of the system failures based on the expert judgments.

2

FMEA analysis of UVG (unmanned ground vehicle) electric powertrain system

Ferencey V., Stanak V., Bugar M.

Zeszyty Naukowe Instytutu Pojazdów / Politechnika Warszawska

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2011

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z. 2/83

17-20

EN

This paper deals with failure modes and effect analysis which is applied to the electric powertrain system of Unmanned Ground Vehicle. Failure Modes and Effects Analysis (FMEA) is a method to analyze potential reliability problems in the development cycle of the project, making it easier to take actions to overcome such issues, enhancing the reliability through design. FMEA is used to identify actions to mitigate the analyzed potential failure modes and their effect on the operations. Anticipating these failure modes, being the central step in the analysis, needs to be carried on extensively, in order to prepare a list of maximum potential failure modes.

PL

Referat pokazuje zastosowanie metody FMEA (FailureModes and Effects Analysis), do analizy niezawodności elektrycznego systemu napędowego Bezzałogowego Pojazdu Taktycznego (UGV - Unmanned Ground Vehicle). FMEA jest metodą analizy potencjalnych problemów z niezawodnością w cyklu rozwoju projektu, co ułatwia podejmowanie działań w celu przezwyciężenia tych problemów i zwiększenie niezawodności poprzez odpowiednie projektowanie. FMEA jest używane do identyfikacji działań w celu analizy potencjalnych awarii i złagodzenia ich wpływu na niezawodność systemu. Ukierunkowanie na przyczyny awarii, jest głównym celem tej analizy, musi być prowadzone intensywnie, w celu przygotowania listy najwyższych dopuszczalnych potencjalnych przyczyn awarii.

3

Identification of virtual computational object's properties by using of screening designs

Pietraszek J., Lisowski E.

Journal of KONES

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2011

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Vol. 18, No. 3

339-346

EN

The paper presents results of investigation on the method of determination of significant properties in the case of virtual computational objects like FEM models. Typical approach is very time-consuming and involves the sequence of meshing and high performance computing. The method proposed in the paper is time-saving by utilization of the experimental design methodology, in particular the screening design analysis. The analysis based on Plackett-Burman designs and fractional factorial designs is focused on the maximum cost reduction. It provides to the main effects analysis. In the mentioned case of the virtual computational object it allows determining significant properties of the model and to focus on the selected most important parameters affecting the key properties of the object. Time-saving is obtained by eliminating insignificant factors from the study area. The mathematical basis for this approach is well known from the experimental design area, and the novelty is the use of it to the particular deterministic computational object. Specific metrics are defined for approximation accuracy, computational cost and their relationship to show the benefits of the method. Identification was carried out in two ways. The first method is used when the two elements are available: the set of output feature values obtained experimentally and chosen error criterion for comparing the predicted and measured values of output characteristics. The second method requires a binding equation or equations, which must be satisfied.