Application of a maximum entropy method to estimate the probability density function of nonlinear or chaotic behavior in structural health monitoring data

2005 ◽  
Author(s):  
Richard A. Livingston ◽  
Shuang Jin
Keyword(s):  
Probability Density Function ◽  
Structural Health Monitoring ◽  
Probability Density ◽  
Maximum Entropy ◽  
Density Function ◽  
Health Monitoring ◽  
Maximum Entropy Method ◽  
Chaotic Behavior ◽  
Entropy Method ◽  
Monitoring Data

Reliability Analysis for Stiffened Plate on the Maximum Entropy Method

2012 ◽  
Vol 525-526 ◽  
pp. 361-364
Author(s):  
Jian He ◽  
Xiao Yan Chen
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Maximum Entropy ◽  
Density Function ◽  
Failure Probability ◽  
Maximum Entropy Method ◽  
Random Variables ◽  
Entropy Method ◽  
Stiffened Plate ◽  
Performance Function

Stiffened plate is widely used in vessel structure because of its high bearing capacity and low weight so the research of failure probability for stiffened plate under explosion load has important engineering meaning. Stiffened plate under near-field explosion is taken as research subject, dynamite density and yield stress of plate are selected as random variables, the original values of one hundred groups of random variables are gotten through the random number generation program, and the moments of random variables are obtained. Based on failure criterion of displacement ductility, the performance function of structure is established, probability density function of performance function is fitted using maximum entropy method then the failure probability of stiffened plate structure is obtained. So as to solve the problem of calculate failure probability when the sample size is small and the probability density function is unknown.

A novel distribution regression approach for data loss compensation in structural health monitoring

2017 ◽  
Vol 17 (6) ◽  
pp. 1473-1490 ◽  
Author(s):  
Zhicheng Chen ◽  
Yuequan Bao ◽  
Hui Li ◽  
Billie F Spencer
Keyword(s):  
Probability Density Function ◽  
Structural Health Monitoring ◽  
Probability Density ◽  
Density Function ◽  
Health Monitoring ◽  
Probability Density Functions ◽  
Density Functions ◽  
Warping Function ◽  
Structural Health ◽  
Regression Approach

Structural health monitoring has arisen as an important tool for managing and maintaining civil infrastructure. A critical problem for all structural health monitoring systems is data loss or data corruption due to sensor failure or other malfunctions, which bring into question in subsequent structural health monitoring data analysis and decision-making. Probability density functions play a very important role in many applications for structural health monitoring. This article focuses on data loss compensation for probability density function estimation in structural health monitoring using imputation methods. Different from common data, continuous probability density functions belong to functional data; the conventional distribution-to-distribution regression technique has significant potential in missing probability density function imputation; however, extrapolation and directly borrowing shape information from the covariate probability density function are the main challenges. Inspired by the warping transformation of distributions in the field of functional data analysis, a new distribution regression approach for imputing missing correlated probability density functions is proposed in this article. The warping transformation for distributions is a mapping operation used to transform one probability density function to another by deforming the original probability density function with a warping function. The shape mapping between probability density functions can be characterized well by warping functions. Given a covariate probability density function, the warping function is first estimated by a kernel regression model; then, the estimated warping function is used to transform the covariate probability density function and obtain an imputation for the missing probability density function. To address issues with poor performance when the covariate probability density function is contaminated, a hybrid approach is proposed that fuses the imputations obtained by the warping transformation approach with the conventional distribution-to-distribution regression approach. Experiments based on field monitoring data are conducted to evaluate the performance of the proposed approach. The corresponding results indicate that the proposed approach can outperform the conventional method, especially in extrapolation. The proposed approach shows good potential to provide more reliable estimation of distributions of missing structural health monitoring data.

Maximum-likelihood maximum-entropy constrained probability density function estimation for prediction of rare events

AIChE Journal ◽  
2014 ◽  
Vol 60 (3) ◽  
pp. 1013-1026 ◽  
Author(s):  
Taha Mohseni Ahooyi ◽  
Masoud Soroush ◽  
Jeffrey E. Arbogast ◽  
Warren D. Seider ◽  
Ulku G. Oktem
Keyword(s):  
Maximum Likelihood ◽  
Probability Density Function ◽  
Probability Density ◽  
Maximum Entropy ◽  
Density Function ◽  
Rare Events ◽  
Function Estimation ◽  
Density Function Estimation

Study on Determining Safety-Monitoring Index for Concrete Dam Based on Cloud Model

2012 ◽  
Vol 226-228 ◽  
pp. 1106-1110 ◽  
Author(s):  
Dong Qin ◽  
Xue Qin Zheng ◽  
Song Lin Wang
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Cloud Model ◽  
Safety Monitoring ◽  
Monitoring Data ◽  
Concrete Dam ◽  
Deterministic Function ◽  
Monitoring Index

The paper, based on analyzing original monitoring data, employs forward and backward cloud algorithm in studying determining safety-monitoring index for concrete dam ,which integrates randomness and fuzziness into of qualitative concept of digital features. By means of above monitoring data, its digital characteristics can be easily transformed to the “quantitative-qualitative- quantitative” change. The final generated quantitative value constitutes the cloud diagram where each droplet demonstrates the characterization of raw monitoring data. At the same time, it also shows the randomness and fuzziness of monitored value. we can study out the safety monitoring indexes according to different remarkable levels by using the probability density function and deterministic function which completed by cloud algorithm. In the end, it is obtained with practice that this method is more suitable and reliability.

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