Wood Product Distribution Parameters for Use in Reliability Analysis

2004 ◽  
Author(s):  
D. S. Gromala ◽  
P. Line
Keyword(s):  
Reliability Analysis ◽  
Product Distribution ◽  
Wood Product ◽  
Distribution Parameters

scholarly journals Reliability Analysis of the New Exponential Inverted Topp–Leone Distribution with Applications

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1662
Author(s):  
Ahmed Sayed M. Metwally ◽  
Amal S. Hassan ◽  
Ehab M. Almetwally ◽  
B M Golam Kibria ◽  
Hisham M. Almongy
Keyword(s):  
Reliability Analysis ◽  
Confidence Intervals ◽  
Hazard Rate ◽  
Shape Parameter ◽  
Quantile Function ◽  
Proposed Model ◽  
Distribution Parameters ◽  
Rate Functions ◽  
New Distribution ◽  
Skewness And Kurtosis

The inverted Topp–Leone distribution is a new, appealing model for reliability analysis. In this paper, a new distribution, named new exponential inverted Topp–Leone (NEITL) is presented, which adds an extra shape parameter to the inverted Topp–Leone distribution. The graphical representations of its density, survival, and hazard rate functions are provided. The following properties are explored: quantile function, mixture representation, entropies, moments, and stress–strength reliability. We plotted the skewness and kurtosis measures of the proposed model based on the quantiles. Three different estimation procedures are suggested to estimate the distribution parameters, reliability, and hazard rate functions, along with their confidence intervals. Additionally, stress–strength reliability estimators for the NEITL model were obtained. To illustrate the findings of the paper, two real datasets on engineering and medical fields have been analyzed.

scholarly journals Fuzzy reliability analysis based on support vector machine

2021 ◽  
Author(s):  
xiao bo Nie ◽  
Haibin Li ◽  
Hongxia Chen ◽  
Ruying Pang ◽  
Honghua Sun
Keyword(s):  
Support Vector Machine ◽  
Probability Distribution ◽  
Reliability Analysis ◽  
Small Sample ◽  
Learning Ability ◽  
Support Vector ◽  
Fuzzy Reliability ◽  
Performance Function ◽  
Sample Data ◽  
Distribution Parameters

Abstract For a structure with implicit performance function structure and less sample data, it is difficult to obtain accurate probability distribution parameters by traditional statistical analysis methods. To address the issue, the probability distribution parameters of samples are often regarded as fuzzy numbers. In this paper, a novel fuzzy reliability analysis method based on support vector machine is proposed. Firstly, the fuzzy variable is converted into an equivalent random variable, and the equivalent mean and equivalent standard deviation are calculated. Secondly, the support vector regression machine with excellent small sample learning ability is used to train the sample data. Subsequently, and the performance function is approximated. Finally, the Monte Carlo method is used to obtain fuzzy reliability. Numerical examples are investigated to demonstrate the effectiveness of the proposed method, which provides a feasible way for fuzzy reliability analysis problems of small sample data.

Effect of Dependent Interval Distribution Parameters on Reliability Prediction

2017 ◽  
Author(s):  
Yao Cheng ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Distribution Parameter ◽  
State Function ◽  
Simulation And Optimization ◽  
Distribution Parameters ◽  
Reliability Methods ◽  
Input Variables ◽  
Independent Distribution ◽  
Interval Distribution

Distributions of input variables of a limit-state function are required for reliability analysis. The distribution parameters are commonly estimated using samples. If some of the samples are in the form of intervals, the estimated distribution parameters will also be given in intervals. Traditional reliability methodologies assume that interval distribution parameters are independent, but as shown in this study, the parameters are actually dependent since they are estimated from the same set of samples. This study investigates the effect of the dependence of distribution parameters on the accuracy of reliability analysis results. The major approach is numerical simulation and optimization. This study indicates that the independent distribution parameter assumption makes the estimated reliability bounds wider than the true bounds due to interval samples. The reason is that the actual combination of the distribution parameters may not include the entire box-type domain assumed by the independent interval parameter assumption. The results of this study not only reveal the cause of the inaccuracy of the independent distribution parameter assumption, but also demonstrate a need of developing new reliability methods to accommodate dependent distribution parameters.

Acidic processing of hemicellulosic saccharides from pine wood: Product distribution and kinetic modeling

2014 ◽  
Vol 162 ◽  
pp. 192-199 ◽  
Author(s):  
Sandra Rivas ◽  
María Jesús González-Muñoz ◽  
Valentín Santos ◽  
Juan Carlos Parajó
Keyword(s):  
Kinetic Modeling ◽  
Product Distribution ◽  
Wood Product ◽  
Pine Wood

scholarly journals Determining Interval Time of Maintenance in Bosowa Cement Indonesia using Reliability Method

2016 ◽  
Vol 3 (2) ◽  
pp. 62
Author(s):  
Muhammad Arsyad Suyuti ◽  
Rusdi Nur
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Operating Conditions ◽  
Target System ◽  
Maintenance Strategies ◽  
Time Period ◽  
Reliability Method ◽  
Distribution Parameters ◽  
Mill Unit ◽  
Finish Mill

Maintenance for machining and production facility is an important aspect to ensure a smooth production process. During this time, it was performed regular maintenance based on technical advice from supplier’s engines which just shows things in general without considering the actual operating conditions. This paper aims to plan the maintenance strategies for the Finish Mill unit based on reliability analysis by considering the target system reliability and cost of improving reliability. The data distribution obtained the most appropriate distribution. Based on the data obtained distribution parameters, then the function of the reliability of each part an be determined so that the value of the reliability of each part and the overall system for a specific time period can be calculated. The results showed that the failure or breakdown Mill Finish Unit was majority caused by the part of 561.BM1, 531.WF1, 531.BC6, 531.BC2, 531.BC1, 561.SR1 and 531.BC3. it means that need to focus o the reliability analysis to allocate their parts.

A Hybrid Reliability Approach Based on Probability and Interval for Uncertain Structures

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
C. Jiang ◽  
X. Han ◽  
W. X. Li ◽  
J. Liu ◽  
Z. Zhang
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Random Variables ◽  
Normal Space ◽  
Uncertain Parameters ◽  
Limited Information ◽  
Reliability Models ◽  
Distribution Parameters ◽  
Hybrid Reliability ◽  
Monotonicity Analysis

Traditional reliability analysis generally uses probability approach to quantify the uncertainty, while it needs a great amount of information to construct precise distributions of the uncertain parameters. In this paper, a new reliability analysis technique is developed based on a hybrid uncertain model, which can deal with problems with limited information. All uncertain parameters are treated as random variables, while some of their distribution parameters are not given precise values but variation intervals. Due to the existence of the interval parameters, a limit-state strip enclosed by two bounding hyper-surfaces will be resulted in the transformed normal space, instead of a single hyper-surface as we usually obtain in conventional reliability analysis. All the limit-state strips are then summarized into two different classes and corresponding reliability analysis models are proposed for them. A monotonicity analysis is carried out for probability transformations of the random variables, through which effects of the interval distribution parameters on the limit state can be well revealed. Based on the monotonicity analysis, two algorithms are then formulated to solve the proposed hybrid reliability models. Three numerical examples are investigated to demonstrate the effectiveness of the present method.

Statistical criteria of stellar population types

1966 ◽  
Vol 24 ◽  
pp. 101-110
Author(s):  
W. Iwanowska
Keyword(s):  
Stellar Population ◽  
Chemical Properties ◽  
Spectrophotometric Study ◽  
Physical And Chemical Properties ◽  
Population Type ◽  
The Galaxy ◽  
Distribution Parameters ◽  
Physical And Chemical ◽  
Statistical Criteria ◽  
And Chemical Properties

In connection with the spectrophotometric study of population-type characteristics of various kinds of stars, a statistical analysis of kinematical and distribution parameters of the same stars is performed at the Toruń Observatory. This has a twofold purpose: first, to provide a practical guide in selecting stars for observing programmes, second, to contribute to the understanding of relations existing between the physical and chemical properties of stars and their kinematics and distribution in the Galaxy.

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