scholarly journals Analysis of selected mathematical models of high-cycle S-N characteristics

2017 ◽  
Vol 3 (20) ◽  
pp. 227-240
Author(s):  
Przemysław Strzelecki ◽  
Janusz Sempruch ◽  
Tomasz Tomaszewski
Keyword(s):  
Fatigue Life ◽  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Least Squares Method ◽  
Construction Materials ◽  
The Other ◽  
Likelihood Method ◽  
Staircase Method ◽  
Aisi 1045 ◽  
Fatigue Characteristics

The paper presents two approaches of determining S-N fatigue characteristics. The first is a commonly used and well-documented approach based on the least squares method and staircase method for limited fatigue life and fatigue limit, accordingly. The other approach employs the maximum likelihood method. The analysis of the parameters obtained through both approaches exhibited minor differences. The analysis was performed for four steel construction materials, i.e. C45+C, 45, SUS630 and AISI 1045. It should be noted that the quantity of samples required in the second approach is significantly smaller than with the first approach, which translates into lower duration and costs of tests.

scholarly journals Experimental Method for Plotting S-N Curve with a Small Number of Specimens

2016 ◽  
Vol 23 (4) ◽  
pp. 129-137 ◽  
Author(s):  
Przemysław Strzelecki ◽  
Janusz Sempruch
Keyword(s):  
Fatigue Life ◽  
Least Squares Method ◽  
Experimental Results ◽  
Bending Test ◽  
Likelihood Method ◽  
Staircase Method ◽  
Random Occurrence ◽  
Straight Line ◽  
Standard Documents ◽  
Time Required

Abstract The study presents two approaches to plotting an S-N curve based on the experimental results. The first approach is commonly used by researchers and presented in detail in many studies and standard documents. The model uses a linear regression whose parameters are estimated by using the least squares method. A staircase method is used for an unlimited fatigue life criterion. The second model combines the S-N curve defined as a straight line and the record of random occurrence of the fatigue limit. A maximum likelihood method is used to estimate the S-N curve parameters. Fatigue data for C45+C steel obtained in the torsional bending test were used to compare the estimated S-N curves. For pseudo-random numbers generated by using the Mersenne Twister algorithm, the estimated S-N curve for 10 experimental results plotted by using the second model, estimates the fatigue life in the scatter band of the factor 3. The result gives good approximation, especially regarding the time required to plot the S-N curve.

scholarly journals Comparing Parameter Estimation of Random Coefficient Autoregressive Model by Frequentist Method

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 62 ◽  
Author(s):  
Autcha Araveeporn
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Mean Square Error ◽  
Maximum Likelihood Method ◽  
Least Squares Method ◽  
Likelihood Function ◽  
Likelihood Method ◽  
Mean Square ◽  
Average Mean Square Error ◽  
Frequentist Method

This paper compares the frequentist method that consisted of the least-squares method and the maximum likelihood method for estimating an unknown parameter on the Random Coefficient Autoregressive (RCA) model. The frequentist methods depend on the likelihood function that draws a conclusion from observed data by emphasizing the frequency or proportion of the data namely least squares and maximum likelihood methods. The method of least squares is often used to estimate the parameter of the frequentist method. The minimum of the sum of squared residuals is found by setting the gradient to zero. The maximum likelihood method carries out the observed data to estimate the parameter of a probability distribution by maximizing a likelihood function under the statistical model, while this estimator is obtained by a differential parameter of the likelihood function. The efficiency of two methods is considered by average mean square error for simulation data, and mean square error for actual data. For simulation data, the data are generated at only the first-order models of the RCA model. The results have shown that the least-squares method performs better than the maximum likelihood. The average mean square error of the least-squares method shows the minimum values in all cases that indicated their performance. Finally, these methods are applied to the actual data. The series of monthly averages of the Stock Exchange of Thailand (SET) index and daily volume of the exchange rate of Baht/Dollar are considered to estimate and forecast based on the RCA model. The result shows that the least-squares method outperforms the maximum likelihood method.

scholarly journals Pendeteksian Outlier pada Regresi Nonlinier dengan Metode statistik Likelihood Displacement

CAUCHY ◽  
2012 ◽  
Vol 2 (3) ◽  
pp. 177
Author(s):  
Siti Tabi'atul Hasanah
Keyword(s):  
Maximum Likelihood ◽  
Nonlinear Regression ◽  
Maximum Likelihood Method ◽  
General Pattern ◽  
The Other ◽  
Likelihood Method ◽  
Initial Hypothesis ◽  
Outlier Data ◽  
Detection Of Outliers ◽  
Likelihood Displacement

<div class="standard"><a id="magicparlabel-1713">Outlier is an observation that much different (extreme) from the other observational data, or data can be interpreted that do not follow the general pattern of the model. Sometimes outliers provide information that can not be provided by other data. That's why outliers should not just be eliminated. Outliers can also be an influential observation. There are many methods that can be used to detect of outliers. In previous studies done on outlier detection of linear regression. Next will be developed detection of outliers in nonlinear regression. Nonlinear regression here is devoted to multiplicative nonlinear regression. To detect is use of statistical method likelihood displacement. Statistical methods abbreviated likelihood displacement (LD) is a method to detect outliers by removing the suspected outlier data. To estimate the parameters are used to the maximum likelihood method, so we get the estimate of the maximum. By using LD method is obtained i.e likelihood displacement is thought to contain outliers. Further accuracy of LD method in detecting the outliers are shown by comparing the MSE of LD with the MSE from the regression in general. Statistic test used is Λ. Initial hypothesis was rejected when proved so is an outlier.</a></div>

scholarly journals Validation and Comparison of Different Statistical Models for the Prediction of Water Main Pipe Breaks in a Municipal Network in Québec, Canada

2016 ◽  
Vol 29 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Sophie Duchesne ◽  
Babacar Toumbou ◽  
Jean-Pierre Villeneuve
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Maximum Likelihood Method ◽  
Least Squares Method ◽  
Likelihood Method ◽  
Water Main ◽  
Pipe Replacement ◽  
The Impact ◽  
Over Time ◽  
Observation Period

In this study, three models for the simulation of the number of breaks in a water main network are presented and compared: linear regression, the Weibull-Exponential-Exponential (WEE), and the Weibull-Exponential-Exponential-Exponential (WEEE) models. These models were calibrated using a database of recorded breaks in a real water main network of a municipality in the province of Québec, for the observation period 1976 to 1996, with the least squares and the maximum likelihood methods. The ability of these models to predict breaks over time was then evaluated by comparing the predicted number of breaks for the years 1997 to 2007 with the observed breaks in the network over the same time period. Results show that if the period of observation is short (around 20 years), calibration of the WEE and WEEE models with the maximum likelihood method leads to estimates that are closer to the observations than when these models are calibrated with the least squares method. When the observation period is longer (around 30 years), the predictions obtained with the models calibrated using the maximum likelihood or the least squares methods are similar. However, the use of the maximum likelihood method for calibration is only possible when data for the occurrence of each break for each pipe of the network are available (a pipe being a homogeneous network segment between two adjacent street junctions). If this is not the case, a trend line will be sufficient to predict the number of breaks over time, though this type of curve does not allow to account for pipe replacement scenarios. If the only information available is the total number of breaks on the network each year, then the impact of replacement scenarios could be simulated with the WEE and WEEE models calibrated using the least squares method.

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