Reliability and Sensitivity Analysis of Mechanical Structures Based on Dimension Reduction Method

2014 ◽  
Vol 635-637 ◽  
pp. 411-416
Author(s):  
Shu Xin Zhang ◽  
Kai Chao Yu
Keyword(s):  
Sensitivity Analysis ◽  
Dimension Reduction ◽  
Reduction Method ◽  
Random Variables ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Dimension Reduction Method ◽  
Distribution Parameters ◽  
Analytic Derivation ◽  
Random Responses

This paper presents a method for probabilistic sensitivity analysis of mechanical components or structural systems subject to random uncertainties in loads, material properties and geometry. The bi-variate dimension reduction method is applied to compute the response moments and their sensitivities with respect to the distribution parameters of basic random variables. Saddlepoint approximations with truncated cumulant generating functions are employed to estimate the probability density functions and cumulative distribution functions of the random responses. The rigorous analytic derivation of the sensitivities of the probability of failure of the systems under consideration with respect to the distribution parameters of basic random variables is derived. Finally, the practicality and efficiency of the proposed method are demonstrated by an application example.

scholarly journals Reliability Sensitivity Analysis Method for Mechanical Components

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yan-Fang Zhang ◽  
Yan-Lin Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Reduction Method ◽  
Dimension Reduction Method ◽  
Edgeworth Series ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Distribution Parameters ◽  
Mechanical Components ◽  
Univariate Dimension Reduction Method

Based on the univariate dimension-reduction method (UDRM), Edgeworth series, and sensitivity analysis, a new method for reliability sensitivity analysis of mechanical components is proposed. The univariate dimension-reduction method is applied to calculate the response origin moments and their sensitivity with respect to distribution parameters (e.g., mean and standard deviation) of fundamental input random variables. Edgeworth series is used to estimate failure probability of mechanical components by using first few response central moments. The analytic formula of reliability sensitivity can be derived by calculating partial derivative of the failure probability P f with respect to distribution parameters of basic random variables. The nonnormal random parameters need not to be transformed into equivalent normal ones. Three numerical examples are employed to illustrate the accuracy and efficiency of the proposed method by comparing the failure probability and reliability sensitivity results obtained by the proposed method with those obtained by Monte Carlo simulation (MCS).

scholarly journals Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach

2011 ◽  
Vol 18 (2) ◽  
pp. 223-234 ◽  
Author(s):  
R. Haas ◽  
K. Born
Keyword(s):  
Complex Terrain ◽  
Large Scale ◽  
Distribution Functions ◽  
Data Availability ◽  
Cumulative Distribution ◽  
Second Step ◽  
Mountain Area ◽  
Investigation Area ◽  
Distribution Parameters ◽  
Mountain Environment

Abstract. In this study, a two-step probabilistic downscaling approach is introduced and evaluated. The method is exemplarily applied on precipitation observations in the subtropical mountain environment of the High Atlas in Morocco. The challenge is to deal with a complex terrain, heavily skewed precipitation distributions and a sparse amount of data, both spatial and temporal. In the first step of the approach, a transfer function between distributions of large-scale predictors and of local observations is derived. The aim is to forecast cumulative distribution functions with parameters from known data. In order to interpolate between sites, the second step applies multiple linear regression on distribution parameters of observed data using local topographic information. By combining both steps, a prediction at every point of the investigation area is achieved. Both steps and their combination are assessed by cross-validation and by splitting the available dataset into a trainings- and a validation-subset. Due to the estimated quantiles and probabilities of zero daily precipitation, this approach is found to be adequate for application even in areas with difficult topographic circumstances and low data availability.

scholarly journals Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Zhao ◽  
Satish V. Ukkusuri ◽  
Jian Lu
Keyword(s):  
Dimension Reduction ◽  
Traffic Flow ◽  
Reduction Method ◽  
Prediction Models ◽  
Urban Traffic ◽  
Dimension Reduction Method ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.

scholarly journals Bi-CGSTAB as an induced dimension reduction method

2010 ◽  
Vol 60 (11) ◽  
pp. 1100-1114 ◽  
Author(s):  
Gerard L.G. Sleijpen ◽  
Peter Sonneveld ◽  
Martin B. van Gijzen
Keyword(s):  
Dimension Reduction ◽  
Reduction Method ◽  
Dimension Reduction Method
Sign in / Sign up

Export Citation Format

Share Document