Application of fuzzy set theory to structural reliability analysis. Method for combining a number of subjective evaluations.

The purpose of this study is to develop a model to investigate the tourists’ preference. Ten attributes of tourist destinations were used in this study. Fuzzy set theory was adopted as the main analysis method to find the tourists’ preference. In this study, 248 pieces of data were used. Besides the evaluations for the factors, the overall evaluations (namely, satisfied, neutral, and dissatisfied) for every tourism destination were also inquired. After screening, 201 pieces of these data could be used. In these 201 pieces of data, 141 were classified into “satisfied” with the tourism destination, accounting for 70.15%, and 49 were “neutral,” accounting for 24.38%, while 11 were “dissatisfied,” accounting for 5.47%. Eight rules were obtained with the method of fuzzy preprocess. Regarding the condition attributes, three of the original ten attributes were found influential, namely, level of prices, living costs, information and tourist services, and tourist safety of the tourism destinations. From the results of this study, it is shown that top management of tourism destinations should put resources in these fields first, in order to allow limited resources to perform to maximum effectiveness.

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Efficient structural reliability analysis method based on advanced Kriging model

Applied Mathematical Modelling ◽

10.1016/j.apm.2014.07.008 ◽

2015 ◽

Vol 39 (2) ◽

pp. 781-793 ◽

Cited By ~ 82

Author(s):

Leigang Zhang ◽

Zhenzhou Lu ◽

Pan Wang

Keyword(s):

Reliability Analysis ◽

Structural Reliability ◽

Kriging Model ◽

Analysis Method ◽

Structural Reliability Analysis

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Fuzzy Logic in Aircraft Onboard Systems Reliability Evaluation—A New Approach

Sensors ◽

10.3390/s21237913 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7913

Author(s):

Andrzej Żyluk ◽

Konrad Kuźma ◽

Norbert Grzesik ◽

Mariusz Zieja ◽

Justyna Tomaszewska

Keyword(s):

Fuzzy Logic ◽

Reliability Analysis ◽

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Probabilistic Model ◽

Expert Knowledge ◽

Operation Time ◽

Theory Approach ◽

Operational Data

This paper is a continuation of research into the possibility of using fuzzy logic to assess the reliability of a selected airborne system. The research objectives include an analysis of statistical data, a reliability analysis in the classical approach, a reliability analysis in the fuzzy set theory approach, and a comparison of the obtained results. The system selected for the investigation was the aircraft gun system. In the first step, after analysing the statistical (operational) data, reliability was assessed using a classical probabilistic model in which, on the basis of the Weibull distribution fitted to the operational data, the basic reliability characteristics were determined, including the reliability function for the selected aircraft system. The second reliability analysis, in a fuzzy set theory approach, was conducted using a Mamdani Type Fuzzy Logic Controller developed in the Matlab software with the Fuzzy Logic Toolbox package. The controller was designed on the basis of expert knowledge obtained by a survey. Based on the input signals in the form of equipment operation time (number of flying hours), number of shots performed (shots), and the state of equipment corrosion (corrosion), the controller determines the reliability of air armament. The final step was to compare the results obtained from two methods: classical probabilistic model and fuzzy logic. The authors have proved that the reliability model using fuzzy logic can be used to assess the reliability of aircraft airborne systems.

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RCA-PCK: A new structural reliability analysis method based on PC-Kriging and radial centralized adaptive sampling strategy

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220957711 ◽

2020 ◽

pp. 095440622095771

Author(s):

Zhenliang Yu ◽

Zhili Sun ◽

Runan Cao ◽

Jian Wang ◽

Yutao Yan

Keyword(s):

Reliability Analysis ◽

Failure Probability ◽

Adaptive Sampling ◽

Structural Reliability ◽

Sampling Strategy ◽

Kriging Model ◽

Strong Nonlinearity ◽

Analysis Method ◽

Structural Reliability Analysis ◽

Training Samples

To improve the efficiency and accuracy of reliability assessment for structures with small failure probability and time-consuming simulation, a new structural reliability analysis method (RCA-PCK) is proposed, which combines PC-Kriging model and radial centralized adaptive sampling strategy. Firstly, the PC-Kriging model is constructed by improving the basis function of Kriging model with sparse polynomials. Then, the sampling region which contributes a great impact on the failure probability is constructed by combining the radial concentration and important sampling technology. Subsequently, the k-means++ clustering technology and learning function LIF are adopted to select new training samples from each subdomains in each iteration. To avoid the sampling distance in one subdomain or the distance between the new training samples in two subdomains being too small, we construct a screening mechanism to ensure that the selected new training samples are evenly distributed in the limit state. In addition, a new convergence criterion is derived based on the relative error estimation of failure probability. Four benchmark examples are given to illustrate the convergence process, accuracy and stability of the proposed method. Finally, the transmission error reliability analysis of thermal-elastic coupled gears is carried out to prove the applicability of the proposed method RCA-PCK to the structures with strong nonlinearity and time-consuming simulation.

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