A Novel Three-parameter Weibull Distribution Parameter Estimation Using Chaos Simulated Annealing Particle Swarm Optimization in Civil Aircraft Risk Assessment

Zhou, Di; Zhuang, Xiao; Zuo, Hongfu

doi:10.1007/s13369-021-05467-0

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, the parameter estimation results of two Weibull distributions are combined with the sample data. Moreover, the cumulative distribution function values calculated from the setting parameters in experiment and estimated parameters by two Weibull distributions are subtracted, with absolute values to obtain the error of each corresponding point 37 . Figure 4A,B illustrates the Weibull plots of the experimental strength dataset of Ti 3 AlC 2 and Ti 3 SiC 2 , with the cumulative distribution curves and the error histogram in Figure 4C,D.…”

Section: Resultsmentioning

confidence: 99%

Statistical analysis of the strength reliability of the MAX phases

Yin

Song

et al. 2022

J Am Ceram Soc.

View full text Add to dashboard Cite

A good comprehension of the mechanical property stability is crucial in promoting the structural applications of the MAX phases. This study aims to determine the most suitable intensity distribution for the experimental flexural strength of present Ti3AlC2 and previous Ti3SiC2, optimize the fitting parameters, and explore the error sources. After comparing two‐parameter Weibull, three‐parameter (3P) Weibull, and other three distributions, the 3P‐Weibull distribution is the most suitable for the MAX phases. The different parameter estimation methods, rank estimators, and minimum used sample numbers of 3P‐Weibull were tested to determine safe and accurate parameter values for engineering application. Furthermore, the 3P‐Weibull modulus (2.32 for Ti3AlC2 and 2.04 for Ti3SiC2) indicates that the MAX phases occupy a middle ground between typical metals and ceramics, attributing to their high fracture toughness and the R‐curve behavior. These findings would open up prospects for the standardization of testing methods for the Weibull statistics of the MAX phases.

show abstract

Section: Resultsmentioning

confidence: 99%

Statistical analysis of the strength reliability of the MAX phases

Yin

Song

et al. 2022

J Am Ceram Soc.

View full text Add to dashboard Cite

show abstract

“…Chaos theory is also introduced to improve the search capability of the algorithm [ 7 ]. However, the algorithm is currently mostly applied to solve problems such as shop scheduling, electric vehicle charging station planning, and Weibull distribution parameter estimation [ 8 ]. There are still few reports on defect detection and quantitative identification.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

Fang

Deng

et al. 2023

Sensors

View full text Add to dashboard Cite

To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

show abstract

Evaluating the lifetime distribution parameters and reliability of products using successive approximation method

Guo,

Kong,

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

The Weibull distribution is an extensively used statistical model for analyzing the reliability of mechanical and electrical components. Due to the complexity of the nonlinear equations and the scarcity of failure data, the common method may not provide satisfactory results of the reliability. In this case, a new approach for Weibull parameter estimation and reliability analysis, based on successive approximation schemes, is presented. The shape and scale parameters are estimated by maximizing the likelihood functions, and the location parameter is obtained by constructing an approximate correction model between it and the failure data. In order to show the performance of the proposed method, an extensive Monte‐Carlo simulation study is conducted. Simulation results show that the proposed method provides better estimates and efficient confidence intervals for Weibull parameters. In addition, the proposed method works well in presence of small sample sizes. Finally, two real examples are analyzed to illustrate the application of the proposed method.

show abstract

A Novel Three-parameter Weibull Distribution Parameter Estimation Using Chaos Simulated Annealing Particle Swarm Optimization in Civil Aircraft Risk Assessment

Cited by 4 publications

References 29 publications

Statistical analysis of the strength reliability of the MAX phases

Statistical analysis of the strength reliability of the MAX phases

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

Evaluating the lifetime distribution parameters and reliability of products using successive approximation method

Contact Info

Product

Resources

About