Parameter distribution characteristics of material fatigue life using improved bootstrap method

2018 ◽  
Vol 28 (5) ◽  
pp. 772-793 ◽  
Author(s):  
Minghui Zhang ◽  
Xintian Liu ◽  
Yansong Wang ◽  
Xiaolan Wang
Keyword(s):  
Fatigue Life ◽  
Test Data ◽  
Bootstrap Method ◽  
Small Sample ◽  
Statistical Characteristics ◽  
Average Error ◽  
Parameter Distribution ◽  
Virtual Test ◽  
Sample Data ◽  
The Bootstrap Method

The bootstrap method is mostly used to estimate statistical characteristics of small sample data. However, the limitations of the bootstrap method itself lead to a reduction in the reliability of small-sample estimates. In this article, an improved bootstrap method is developed to address this problem. In the statistically significant error range (the sample average error and the limit error of sampling) of the original single sample data, expanding the virtual test data that obey two distributions to overcome the limitations of the bootstrap method itself. This article compares and analyses these two methods through the case; the result indicates that the improved bootstrap method can enhance the reliability of the estimation results without changing its probability distribution. We also discussed how to reduce the fluctuation of the improved bootstrap method. And the effectiveness and feasibility of this improved method are discussed in the analysis of fatigue life test data.

scholarly journals The Smoothed Bootstrap Fine-Tuning

2019 ◽  
Vol 1 (1) ◽  
pp. 716-723
Author(s):  
Renata Dwornicka ◽  
Andrii Goroshko ◽  
Jacek Pietraszek
Keyword(s):  
Probability Distribution ◽  
Empirical Distribution ◽  
Bootstrap Method ◽  
Small Sample ◽  
Fine Tuning ◽  
Smoothed Bootstrap ◽  
Full Probability ◽  
The Bootstrap Method

AbstractThe bootstrap method is a well-known method to gather a full probability distribution from the dataset of a small sample. The simple bootstrap i.e. resampling from the raw dataset often leads to a significant irregularities in a shape of resulting empirical distribution due to the discontinuity of a support. The remedy for these irregularities is the smoothed bootstrap: a small random shift of source points before each resampling. This shift is controlled by specifically selected distributions. The key issue is such parameter settings of these distributions to achieve the desired characteristics of the empirical distribution. This paper describes an example of this procedure.

scholarly journals Mango Grading System Based on Optimized Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bin Zheng ◽  
Tao Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Small Sample ◽  
Parameters Optimization ◽  
Average Error ◽  
Grading System ◽  
Data Set ◽  
Operation Speed ◽  
Sample Data

In order to achieve the accuracy of mango grading, a mango grading system was designed by using the deep learning method. The system mainly includes CCD camera image acquisition, image preprocessing, model training, and model evaluation. Aiming at the traditional deep learning, neural network training needs a large number of sample data sets; a convolutional neural network is proposed to realize the efficient grading of mangoes through the continuous adjustment and optimization of super-parameters and batch size. The ultra-lightweight SqueezeNet related algorithm is introduced. Compared with AlexNet and other related algorithms with the same accuracy level, it has the advantages of small model scale and fast operation speed. The experimental results show that the convolutional neural network model after super-parameters optimization and adjustment has excellent effect on deep learning image processing of small sample data set. Two hundred thirty-four Jinhuang mangoes of Panzhihua were picked in the natural environment and tested. The analysis results can meet the requirements of the agricultural industry standard of the People’s Republic of China—mango and mango grade specification. At the same time, the average accuracy rate was 97.37%, the average error rate was 2.63%, and the average loss value of the model was 0.44. The processing time of an original image with a resolution of 500 × 374 was only 2.57 milliseconds. This method has important theoretical and application value and can provide a powerful means for mango automatic grading.

PROBABILISTIC SPECIMEN PROPERTY-FATIGUE LIFE MAPPING AND P-S-N CURVE FITTING

2013 ◽  
Vol 20 (05) ◽  
pp. 1350020
Author(s):  
LIYANG XIE ◽  
JIANZHONG LIU ◽  
NINGXIANG WU ◽  
WENXUE QIAN
Keyword(s):  
Fatigue Life ◽  
Test Data ◽  
Stress Level ◽  
Curve Fitting ◽  
Cyclic Stress ◽  
Small Sample ◽  
Test Method ◽  
Life Distribution ◽  
Stress Levels ◽  
Curve Fitting Method

Fitting P-S-N curve with small-size sample of fatigue test data is significant in engineering applications. Although several small sample-based P-S-N curve fitting methods have been developed, complexity in mathematics and/or the unrealistic assumption of the methods hinder their application seriously. Based on the principle of probabilistically mapping from the probability distribution of specimen property to that of fatigue life of the specimen, this paper presents a new, easy to apply P-S-N curve fitting method. By collecting the life distribution information dispersed in several small-size samples of fatigue lives tested under different cyclic stress levels, a large-size sample of equivalent fatigue life data can be built based on the mapping mechanism as well as the uniqueness of the relationship between fatigue life standard deviation and cyclic stress level. The basic viewpoint is that the fatigue lives tested at any cyclic stress levels can be equivalently converted to an arbitrary baseline stress level according to the life distribution–stress relationship, and this principle can be applied to determine the P-S-N curves with a limited number of test data. Test results illustrate that the P-S-N curves obtained by such methods with 30, 24 or 20 samples, respectively, are close to those obtained by the conventional test method with 60 or 40 samples.

New probabilistic S-N curves modeling method with small-sample test data of composite materials

2021 ◽  
pp. 095440622199007
Author(s):  
Qiang Ma ◽  
Zongwen An ◽  
Xuezong Bai ◽  
Huidong Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Fatigue Life ◽  
Composite Materials ◽  
Fatigue Test ◽  
Test Data ◽  
Small Sample ◽  
Swarm Optimization ◽  
Conversion Model ◽  
Large Dispersion ◽  
Sample Test

Considering the large dispersion when processing small-sample fatigue test data of composite materials, a new method for modeling probabilistic S- N curves is proposed in terms of the equivalent fatigue lives. An equivalent fatigue life conversion model is first established based on two fundamental assumptions to improve the small-sample information utilized. Subsequently, a backward statistical inference technique improved by particle swarm optimization is used to determine probabilistic S- N curves through the equivalent fatigue lives. Finally, the proposed method is verified in terms of precision and stability by the fatigue test data of carbon eight-harness-satin/epoxy laminate. The results indicate that the proposed method can offer an accurate description of the probabilistic behavior of composite materials with small-sample test data.

A New Approach to Finding a Risk-Informed Safety Factor for “Fail-Safe” Pressure Vessel and Piping Design

2015 ◽  
Vol 750 ◽  
pp. 3-23
Author(s):  
Jeffrey T. Fong ◽  
N. Alan Heckert ◽  
James J. Filliben ◽  
Pedro V. Marcal ◽  
Stephen W. Freiman
Keyword(s):  
Fatigue Life ◽  
Safety Factor ◽  
Bootstrap Method ◽  
Glass Ceramics ◽  
High Strength ◽  
Engineering System ◽  
New Approach ◽  
Fatigue Life Estimation ◽  
Fail Safe ◽  
The Bootstrap Method

The purpose of this paper is to present a new approach to finding a risk-informed safety factor for the “fail-safe” design of a high-consequence engineering system. The new approach is based on the assumption of a 99.99 % confidence level and a 99.99 % coverage, and the application of the classical theory of tolerance limits, error propagation, and a method of statistical model parameter estimation known as the bootstrap method. To illustrate this new approach, we first apply the methodology to theUTSdata of six materials ranging from glass, ceramics, to a high-strength steel at both 20 C and 600 C, and then to the fatigue life estimation of a BK-7 glass using two available additional sets of laboratory test data. Significance and limitations of our new approach to the “fail-safe”UTSdesign and fatigue life prediction of an aging PVP or aircraft are presented and discussed.

Design Allowable for Durability Analysis of Advanced Composites

2010 ◽  
Vol 160-162 ◽  
pp. 395-400
Author(s):  
Xue Ling Fan ◽  
Wen Jun Qin
Keyword(s):  
Fatigue Life ◽  
Test Data ◽  
Composite Structures ◽  
Small Sample ◽  
Test Method ◽  
Test Statistic ◽  
Significance Level ◽  
Advanced Composites ◽  
Durability Analysis ◽  
Standard Design

An allowable design method is presented, along with its experimental validation, to obtain engineering-standard design allowable for fatigue life evaluation of advanced composites. A small sample test method for stress/life curves was established to overcome these issues based on the regression analysis theory. Statistically-based design values, such as the B-basis value, the Anderson-Darling test statistic and the observed significance level, were obtained depends on the characteristics of the composite material test data. For evaluation of the fatigue life of advanced composites, the S-N curve-fitting parameters are obtained using the linear heteroscedastic regression method based on experimental data. Another advantage of this method is that all the test data at different stress levels can be analyzed coinstantaneously, which greatly saved the experimental efforts and specimen for confidential statistical results. Finally, an experimental test program has been conducted on typical laminate composites to generate statistically meaningful data for durability design. The numerical results show that the proposed method can be used as a guide to obtaining design allowable for durability analysis of advanced composite structures.

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