The Extreme Value Distribution and Application of Decarburization of Piston Rod

2021 ◽  
Vol 105 ◽  
pp. 125-133
Author(s):  
Jin Zhe Chen ◽  
Ge Ping Bi
Keyword(s):  
Parameter Estimation ◽  
Data Collection ◽  
Return Period ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Maximum Depth ◽  
Value Prediction ◽  
Maximum Value

Gumbel extreme value distribution is used to predict the maximum depth of decarburization of piston rod. The results show that: 1) The prediction maximum depth of decarburization of piston rod should include four steps: data collection, parameter estimation, distribution test and maximum value prediction. 2) The maximum depth of decarburization of piston rod consistent with Gumbel minimum distribution. 3) When the return period is 1000, the predicted maximum depth of decarburization is (0.12 ± 0.01) mm, (k = 2).

scholarly journals A study on the extreme value distribution of the minimum tensile strength of bolt

2021 ◽  
Vol 269 ◽  
pp. 03003
Author(s):  
Geping Bi ◽  
Jinzhe Chen ◽  
Wenhua Tai
Keyword(s):  
Tensile Strength ◽  
Parameter Estimation ◽  
Data Collection ◽  
Production Process ◽  
Product Quality ◽  
Return Period ◽  
Extreme Value Distribution ◽  
Value Distribution ◽  
Minimum Value ◽  
Extreme Distribution

Taking M12.5 × 1.25 × 65-10.9 bolt as an example, this paper studies the extreme distribution of the minimum value of bolt tensile strength in order to evaluate the reliability and stability of bolt product quality and to verify the production process. Through data collection, parameter estimation, distribution test and extreme prediction, it is concluded that: 1) the distribution of the minimum value of bolt tensile strength conforms to Gumbel extreme distribution; 2) when the return period is 10000, the predicted minimum tensile strength is 1071.7 MPa ± 3.2 MPa, k = 2. It is higher than the minimum value of 1040 MPa required by ISO 898-1 standard.

Semi-Parametric Statistical Model for Extreme Value Statistical Models and Application in Automatic Control

2014 ◽  
Vol 680 ◽  
pp. 455-458
Author(s):  
Yu Han
Keyword(s):  
Parameter Estimation ◽  
Automatic Control ◽  
Statistical Models ◽  
Estimation Method ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Vector Model ◽  
Extreme Statistics ◽  
The Impact

The frequency that extreme events appear in the life is low,but once it appears,the impact will be significant; many scholars have conducted in depth research and found that statistical theory of extreme value. The theory of extreme statistics plays a more and more important role in many fields such as automatic control, assembly line etc. This paper,makes an in-depth research towards the characteristics and parameter estimation of the extreme value statistical models,as well as the application,mainly analyzes the Bayes parameter estimation method of extreme value distribution,the extreme value distribution theory and Copula function random vector model.

scholarly journals Extreme Storm Surge estimation and projection through the Metastatistical Extreme Value Distribution

2021 ◽  
Author(s):  
Maria Francesca Caruso ◽  
Marco Marani
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Return Period ◽  
Extreme Value Distribution ◽  
Storm Surges ◽  
Extreme Value ◽  
Value Distribution ◽  
Sample Sizes ◽  
Sea Levels ◽  
Calibration Sample

Abstract. Accurate estimates of the probability of extreme sea levels are pivotal for assessing risk and the design of coastal defense structures. This probability is typically estimated by modelling observed sea-level records using one of a few statistical approaches. In this study we comparatively apply the Generalized Extreme Value (GEV) distribution, based on Block Maxima (BM) and Peak-Over-Threshold (POT) formulations, and the recently Metastatistical Extreme Value Distribution (MEVD) to four long time series of sea-level observations distributed along European coastlines. A cross-validation approach, dividing available data in separate calibration and test sub-samples, is used to compare their performances in high-quantile estimation. To address the limitations posed by the length of the observational time series, we quantify the estimation uncertainty associated with different calibration sample sizes, from 5 to 30 years. Focusing on events with a high return period, we find that the GEV-based approaches and MEVD perform similarly when considering short samples (5 years), while the MEVD estimates outperform the traditional methods when longer calibration sample sizes (10-30 years) are considered. We then investigate the influence of sea-level rise through 2100 on storm surges frequencies. The projections indicate an increase in the height of storm surges for a fixed return period that are spatially heterogeneous across the coastal locations explored.

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