Reliability Analysis of Thermal Warping Deformation of Brake Disc Based on AK-MCS Method

2016 ◽

Cited By ~ 1

Author(s):

Zhenhui Zhan ◽

Xianmin Zhang

Keyword(s):

Reliability Analysis ◽

Parallel Manipulator ◽

Random Variable ◽

Parallel Manipulators ◽

Analysis Method ◽

Motion Error ◽

Interval Variables ◽

First Order Second Moment ◽

Joint Clearances ◽

Mcs Method

A general methodology for motion error and motion reliability analysis of planar parallel manipulators (PPMs) with random and interval variables is presented. The inherent uncertainties of the manipulator, including tolerances in manufactures, errors in inputs as well as joint clearances are taken into account. The error model of a 3-RRR parallel manipulator is built and the global sensitivity coefficients of motion errors to variations are defined and obtained. The joint clearances are treated as interval variables while the others are treated as random variables. As a result, the motion error of the manipulator could turn out to be the mixture of a random variable and an interval variable. A new motion reliability analysis method based on the First Order Second Moment (FOSM) method and the Monte Carlo simulation (MCS) method is developed for the manipulator with random and interval variables. This paper provides a new idea to better understand the motion reliability affected by the inherent uncertainties of PPMs.

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KNN Classification Based MCS Method of Structural Reliability Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.638-640.136 ◽

2014 ◽

Vol 638-640 ◽

pp. 136-139 ◽

Cited By ~ 1

Author(s):

Ying Zhao ◽

Guo Shao Su ◽

Liu Bin Yan

Keyword(s):

Reliability Analysis ◽

Failure Probability ◽

Structural Reliability ◽

Simulation Method ◽

Superior Performance ◽

Performance Function ◽

Training Samples ◽

Study Results ◽

Knn Classification ◽

Mcs Method

A KNN Classification Based MCS (Monte Carlo Simulation Method) is proposed for the reliability analysis which hindered by the implicit nature of the performance function. In the method, Markov chain is adopted to simulate a small amount of training samples, KNN classification is used to generate surrogate model of performance function, MCS is used to estimate the failure probability. An iterative algorithm is presented to improve surrogate precision dynamically in the region contributing to the failure probability significantly. The study results demonstrate that the proposed method has superior performance to the traditional response surface method.

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An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models

Periodica Polytechnica Civil Engineering ◽

10.3311/ppci.12747 ◽

2019 ◽

Author(s):

Jafar Vahedi ◽

Mohammad Reza Ghasemi ◽

Mahmoud Miri

Keyword(s):

Reliability Analysis ◽

Surrogate Model ◽

Numerical Models ◽

Reliability Assessment ◽

Simulation Method ◽

Adaptive Method ◽

Kriging Surrogate Model ◽

Constant Regression ◽

Training Samples ◽

Mcs Method

Meta-models or surrogate models are convenient tools for reliability assessment of problems with time-consuming numerical models. Recently, an adaptive method called AK-MCS has been widely used for reliability analysis by combining Mont-Carlo simulation method and Kriging surrogate model. The AK-MCS method usually uses constant regression as a Kriging trend. However, other regression trends may have better performance for some problems. So, a method is proposed by combining multiple Kriging meta-models with various trends. The proposed method is based on the maximum entropy of predictions to select training samples. Using multiple Kriging models can reduce the sensitivity to the regression trend. So, the propped method can have better performance for different problems. The proposed method is applied to some examples to show its efficiency.

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Practical reliability analysis of slope stability by advanced Monte Carlo simulations in a spreadsheet

Canadian Geotechnical Journal ◽

10.1139/t10-044 ◽

2011 ◽

Vol 48 (1) ◽

pp. 162-172 ◽

Cited By ~ 160

Author(s):

Yu Wang ◽

Zijun Cao ◽

Siu-Kui Au

Keyword(s):

Monte Carlo ◽

Slope Stability ◽

Spatial Variability ◽

Soil Properties ◽

Reliability Analysis ◽

Slip Surface ◽

Probability Of Failure ◽

Critical Slip Surface ◽

Microsoft Excel ◽

Mcs Method

This paper develops a Monte Carlo simulation (MCS)-based reliability analysis approach for slope stability problems and utilizes an advanced MCS method called “subset simulation” for improving efficiency and resolution of the MCS at relatively small probability levels. Reliability analysis is operationally decoupled from deterministic slope stability analysis and implemented using a commonly available spreadsheet software, Microsoft Excel. The reliability analysis spreadsheet package is validated through comparison with other reliability analysis methods and commercial software. The spreadsheet package is then used to explore the effect of spatial variability of the soil properties and critical slip surface. It is found that, when spatial variability of soil properties is ignored by assuming perfect correlation, the variance of the factor of safety (FS) is overestimated, which may result in either over (conservative) or under (unconservative) estimation of the probability of failure (Pf = P(FS < 1)). When the spatial variability of soil properties is considered, the critical slip surface varies spatially and such spatial variability should be properly accounted for. Otherwise, the probability of failure can be significantly underestimated and unconservative.

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