Evidence-theory-based kinematic uncertainty analysis of a dual crane system with epistemic uncertainty

2021 ◽  
pp. 1-29
Author(s):  
Bin Zhou ◽  
Bin Zi ◽  
Yishang Zeng ◽  
Weidong Zhu
Keyword(s):  
Uncertainty Analysis ◽  
Perturbation Method ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Taylor Series Expansion ◽  
Computational Time ◽  
Evidence Based ◽  
Interval Equations ◽  
Basic Probability ◽  
Interval Perturbation Method

Abstract An evidence-theory-based interval perturbation method (ETIPM) and an evidence-theory-based subinterval perturbation method (ETSPM) are presented for the kinematic uncertainty analysis of a dual cranes system (DCS) with epistemic uncertainty. A multiple evidence variable (MEV) model that consists of evidence variables with focal elements (FEs) and basic probability assignments (BPAs) is constructed. Based on the evidence theory, an evidence-based kinematic equilibrium equation with the MEV model is equivalently transformed to several interval equations. In the ETIPM, the bounds of the luffing angular vector (LAV) with respect to every joint FE are calculated by integrating the first-order Taylor series expansion and interval algorithm. The bounds of the expectation and variance of the LAV and corresponding BPAs are calculated by using the evidence-based uncertainty quantification method. In the ETSPM, the subinterval perturbation method is introduced to decompose original FE into several small subintervals. By comparing results yielded by the ETIPM and ETSPM with those by the evidence theory-based Monte Carlo method, numerical examples show that the accuracy and computational time of the ETSPM are higher than those of the ETIPM, and the accuracy of the ETIPM and ETSPM can be significantly improved with the increase of the number of FEs and subintervals.

scholarly journals Evidential Model Validation under Epistemic Uncertainty

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Deng ◽  
Xi Lu ◽  
Yong Deng
Keyword(s):  
Computational Model ◽  
Model Validation ◽  
Bayes Factor ◽  
Epistemic Uncertainty ◽  
Probability Distributions ◽  
Uncertainty Propagation ◽  
Evidence Theory ◽  
Validation Method ◽  
Basic Probability ◽  
The Given

This paper proposes evidence theory based methods to both quantify the epistemic uncertainty and validate computational model. Three types of epistemic uncertainty concerning input model data, that is, sparse points, intervals, and probability distributions with uncertain parameters, are considered. Through the proposed methods, the given data will be described as corresponding probability distributions for uncertainty propagation in the computational model, thus, for the model validation. The proposed evidential model validation method is inspired by the idea of Bayesian hypothesis testing and Bayes factor, which compares the model predictions with the observed experimental data so as to assess the predictive capability of the model and help the decision making of model acceptance. Developed by the idea of Bayes factor, the frame of discernment of Dempster-Shafer evidence theory is constituted and the basic probability assignment (BPA) is determined. Because the proposed validation method is evidence based, the robustness of the result can be guaranteed, and the most evidence-supported hypothesis about the model testing will be favored by the BPA. The validity of proposed methods is illustrated through a numerical example.

An efficient epistemic uncertainty analysis method for structural-acoustic problem based on evidence theory

2018 ◽  
Vol 66 (2) ◽  
pp. 117-130
Author(s):  
Ning Chen ◽  
Shengwen Yin ◽  
Dejie Yu ◽  
Zuojun Liu ◽  
Baizhan Xia
Keyword(s):  
Uncertainty Analysis ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Analysis Method ◽  
Acoustic Problem

Uncertainty Analysis With Probability and Evidence Theories

2006 ◽  
Author(s):  
Xiaoping Du
Keyword(s):  
Probability Theory ◽  
Uncertainty Analysis ◽  
Physical System ◽  
Epistemic Uncertainty ◽  
Evidence Theory ◽  
Future Research ◽  
Aleatory Uncertainty ◽  
Maximum Probability ◽  
Reliability Method ◽  
Input Parameters

Both aleatory and epistemic uncertainties exist in engineering applications. Aleatory uncertainty (objective or stochastic uncertainty) describes the inherent variation associated with a physical system or environment. Epistemic uncertainty, on the other hand, is derived from some level of ignorance or incomplete information about a physical system or environment. Aleatory uncertainty associated with parameters is usually modeled by probability theory and has been widely researched and applied by industry, academia, and government. The study of epistemic uncertainty in engineering has recently started. The feasibility of the unified uncertainty analysis that deals with both types of uncertainties is investigated in this paper. The input parameters with aleatory uncertainty are modeled with probability distributions by probability theory, and the input parameters with epistemic uncertainty are modeled with basic probability assignment by evidence theory. The effect of the mixture of both aleatory and epistemic uncertainties on the model output is modeled with belief and plausibility measures (or the lower and upper probability bounds). It is shown that the calculation of belief measure or plausibility measure can be converted to the calculation of the minimum or maximum probability of failure over each of the mutually exclusive subsets of the input parameters with epistemic uncertainty. A First Order Reliability Method (FORM) based algorithm is proposed to conduct the unified uncertainty analysis. Two examples are given for the demonstration. Future research directions are derived from the discussions in this paper.

Uncertainty Analysis in Fatigue Life Prediction of Concrete Using Evidence Theory

2016 ◽  
Vol 866 ◽  
pp. 25-30
Author(s):  
He Sheng Tang ◽  
Jia He Mei ◽  
Wei Chen ◽  
Da Wei Li ◽  
Song Tao Xue
Keyword(s):  
Experimental Data ◽  
Fatigue Life ◽  
Uncertainty Analysis ◽  
Model Uncertainty ◽  
Life Prediction ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Evidence Theory ◽  
Fatigue Life Prediction ◽  
Theory Method

Various sources of uncertainty exist in concrete fatigue life prediction, such as variability in loading conditions, material parameters, experimental data and model uncertainty. In this article, the uncertainty model of concrete fatigue life prediction based on the S-N curve is built, and the evidence theory method is presented for uncertainty analysis in fatigue life prediction of concrete while considering the epistemic uncertainty of the parameter of the model. Based on the experimental of concrete four-point bending beams, the evidence theory method is applied to quantify the epistemic uncertainty stem from experimental data and model uncertainty. To improve the efficiency of computation, a method of differential evolution is adopted to speedup the works of uncertainty propagation. The efficiency and feasibility of the proposed approach are verified through a comparative analysis of probability theory.

scholarly journals An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021 ◽  
Author(s):  
Zezheng Yan ◽  
Hanping Zhao ◽  
Xiaowen Mei
Keyword(s):  
Evidence Theory ◽  
Combination Method ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Combination Methods ◽  
Conflicting Evidence ◽  
Basic Probability ◽  
The Right ◽  
Conflict Intensity ◽  
Dempster’S Rule Of Combination

AbstractDempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

scholarly journals Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 820
Author(s):  
Jingyu Liu ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Multi Agent System ◽  
Agent System ◽  
Combination Rules ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Reasonable Evidence ◽  
Basic Probability ◽  
Multi Agent

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.

scholarly journals Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

2018 ◽  
Vol 14 (04) ◽  
pp. 4
Author(s):  
Xuemei Yao ◽  
Shaobo Li ◽  
Yong Yao ◽  
Xiaoting Xie
Keyword(s):  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Monitoring And Diagnosis ◽  
Multi Sensor Data Fusion ◽  
Basic Probability ◽  
Optimal Fusion ◽  
Single Sensor ◽  
Mechanical Devices

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

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