Approaches to Achieving High Reliability and Confidence Levels with Small Test Sample Sizes

2015 ◽  
Vol 8 (2) ◽  
pp. 343-354 ◽  
Author(s):  
Zhigang Wei ◽  
Robert Rebandt ◽  
Michael Start ◽  
Litang Gao ◽  
Jason Hamilton ◽  
...  
Keyword(s):  
High Reliability ◽  
Test Sample ◽  
Sample Sizes ◽  
Confidence Levels ◽  
Small Test

Synthetic Reliability Assessment Model involving Temperature–Humidity Step-Stress Based on Wiener Process

2020 ◽  
pp. 2150016
Author(s):  
Bin Suo ◽  
Liang Zhao
Keyword(s):  
Wiener Process ◽  
High Reliability ◽  
Likelihood Estimation ◽  
Degradation Process ◽  
Test Sample ◽  
Assessment Model ◽  
Engineering Practice ◽  
Accelerated Degradation ◽  
Failure Data ◽  
Newton Iteration Method

There are always some difficulties in storage reliability evaluation of high-reliability, long-life, and high-value products, such as the test sample being small, degradation speed being slow, and failure data being inadequate. Temperature–humidity step-stress accelerated degradation test (THSS-ADT) is an effective method to evaluate the reliability of this type of products, but the test data processing is an extremely complex work. The motivation of this paper is to provide a clear, effective, and convenient method to evaluate the reliability on the basis of THSS-ADT data. Considering the stochastic volatility in degradation process, Wiener process is used to modeling the accelerated degradation process. The methods to estimate the parameters of Peck accelerated model and degradation model are discussed under temperature–humidity step-stress. As ordinary optimization algorithms (such as Newton Iteration Method and impelling function method) find it difficult to get the solutions, particle swarm optimization (PSO) method is used to solve the problem of maximum-likelihood estimation. Finally, the proposed methods are demonstrated for two examples, in which one is a numerical simulation, and another is an engineering practice of a microwave power amplifier.

Use of Loose Shelled Kernels to Estimate Aflatoxin in Farmers Stock Peanut Lots1

1999 ◽  
Vol 26 (1) ◽  
pp. 39-44 ◽  
Author(s):  
T. B. Whitaker ◽  
F. G. Giesbrecht ◽  
W. M. Hagler
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Functional Relationship ◽  
Test Sample ◽  
Sampling Plan ◽  
Sample Sizes ◽  
Total Aflatoxin ◽  
Aflatoxin Concentration

Abstract Loose shelled kernels (LSK) are a defined grade component of farmers stock peanuts and represented, on the average, 33.3% of the total aflatoxin mass and 7.7% of the kernel mass among the 120 farmers stock peanut lots studied. The functional relationship between aflatoxin in LSK taken from 2-kg test samples and the aflatoxin in farmers stock peanut lots was determined to be linear with zero intercept and a slope of 0.297. The correlation between aflatoxin in LSK and aflatoxin in the lot was 0.844 which suggests that LSK taken from large test samples can be used to estimate the aflatoxin concentration in a farmer's lot. Using only LSK allows large test samples to be used to estimate the lot concentration since LSK can be easily screened from a large test sample. If LSK accounts for 7.7% of the lot kernel mass, a 50-kg sample will yield about 3.9 kg of LSK which can be easily prepared for aflatoxin analysis. Increasing the test sample size from 2 to 50 kg reduced the coefficient of variation associated with estimating a lot with 100 parts per billion (ppb) aflatoxin from 114 to 23%, respectively. As an example, a farmers stock aflatoxin sampling plan with dual tolerances (10 and 100 ppb) that classified lots into three categories was evaluated for two test sample sizes (2 and 50 kg). The effect of increasing test sample size from 2 to 50 kg on the number of lots classified into each of the three categories was demonstrated when measuring aflatoxin only in LSK.

Mechanical Reliability Confidence Limits

1978 ◽  
Vol 100 (4) ◽  
pp. 607-612 ◽  
Author(s):  
D. Kececioglu ◽  
G. Lamarre
Keyword(s):  
Sample Size ◽  
Confidence Limit ◽  
Effective Sample Size ◽  
Confidence Limits ◽  
Mechanical Reliability ◽  
Sample Sizes ◽  
Confidence Levels ◽  
Standard Deviations ◽  
Strength Distributions

Charts are presented relating the lower one-sided confidence limit on the reliability, RL1, to the effective sample size, ne, calculated from the sample sizes used to estimate the failure governing stress and strength distributions, or f(s) and f(S) respectively, and a factor K which is a function of the estimated means and standard deviations of f(s) and f(S). These graphs cover an ne range of 5 to 1000, confidence levels of 0.80, 0.90, 0.95, and 0.99, and lower one-sided limits on the reliability of 0.85 to 0.9145. The equations used to develop these charts are derived and two examples of their applications are given.

scholarly journals Precise radial velocities of giant stars

2018 ◽  
Vol 616 ◽  
pp. A33 ◽  
Author(s):  
Stephan Stock ◽  
Sabine Reffert ◽  
Andreas Quirrenbach
Keyword(s):  
Test Sample ◽  
Evolutionary Stage ◽  
Evolutionary Models ◽  
Planetary Evolution ◽  
Giant Stars ◽  
Small Test ◽  
Red Giant ◽  
Stellar Masses ◽  
Host Stars

Context. The determination of accurate stellar parameters of giant stars is essential for our understanding of such stars in general and as exoplanet host stars in particular. Precise stellar masses are vital for determining the lower mass limit of potential substellar companions with the radial velocity method, but also for dynamical modeling of multiplanetary systems and the analysis of planetary evolution. Aims. Our goal is to determine stellar parameters, including mass, radius, age, surface gravity, effective temperature and luminosity, for the sample of giants observed by the Lick planet search. Furthermore, we want to derive the probability of these stars being on the horizontal branch (HB) or red giant branch (RGB), respectively. Methods. We compare spectroscopic, photometric and astrometric observables to grids of stellar evolutionary models using Bayesian inference. Results. We provide tables of stellar parameters, probabilities for the current post-main sequence evolutionary stage, and probability density functions for 372 giants from the Lick planet search. We find that 81% of the stars in our sample are more probably on the HB. In particular, this is the case for 15 of the 16 planet host stars in the sample. We tested the reliability of our methodology by comparing our stellar parameters to literature values and find very good agreement. Furthermore, we created a small test sample of 26 giants with available asteroseismic masses and evolutionary stages and compared these to our estimates. The mean difference of the stellar masses for the 24 stars with the same evolutionary stages by both methods is only ΔM = 〈Mtrk. − MAst.〉 = 0.01 ± 0.20 M⊙. Conclusions. We do not find any evidence for large systematic differences between our results and estimates of stellar parameters based on other methods. In particular we find no significant systematic offset between stellar masses provided by asteroseismology to our Bayesian estimates based on evolutionary models.

scholarly journals Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function

2021 ◽  
Vol 11 (23) ◽  
pp. 11563
Author(s):  
Yan-Feng Li ◽  
Ming Huang ◽  
Song Bai ◽  
Yuan Chen ◽  
Hong-Zhong Huang
Keyword(s):  
Distribution Function ◽  
Reliability Analysis ◽  
Attitude Control ◽  
High Reliability ◽  
Test Sample ◽  
Copula Function ◽  
Great Difficulty ◽  
Accelerated Life Test ◽  
Attitude Control System ◽  
Modeling And Analysis

The momentum wheel is a key component of the satellite attitude control system and has a direct impact on the reliability and overall life of the satellite. The momentum wheel has the characteristics of a high reliability, long life, and complex failure mechanics, which leads to expensive maintenance and a low reliability of the test sample. Therefore, it is challenge to implement an accelerated life test. The traditional life data statistical method has great difficulty in solving the reliability analysis of the momentum wheel. A reliability calculation method based on copula function for multi-degradation is proposed. Firstly, the key factors affecting the reliability of the momentum wheel are analyzed, and the lubricant residual quantity and current are selected as the degradation quantity. Secondly, the wiener process is used to model the degradation of a single degradation quantity, and the edge distribution function of the momentum wheel reliability is obtained. Considering that the correlation between multiple degradation quantities has a non-negligible influence on the reliability analysis result, the copula function is introduced to describe the correlation, and the edge distributions are fused to obtain the joint distribution function of the momentum wheel reliability.

scholarly journals Estimation of Li‐Ion Degradation Test Sample Sizes Required to Understand Cell‐to‐Cell Variability**

2021 ◽  
Author(s):  
Philipp Dechent ◽  
Samuel Greenbank ◽  
Felix Hildenbrand ◽  
Saad Jbabdi ◽  
Dirk Uwe Sauer ◽  
...  
Keyword(s):  
Test Sample ◽  
Sample Sizes ◽  
Li Ion ◽  
Degradation Test ◽  
Cell To Cell Variability

scholarly journals How sample size influences the replicability of task-based fMRI

2017 ◽  
Author(s):  
Benjamin O. Turner ◽  
Erick J. Paul ◽  
Michael B. Miller ◽  
Aron K. Barbey
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Large Range ◽  
Test Sample ◽  
Small Samples ◽  
Sample Sizes ◽  
Typical Sample ◽  
The Impact ◽  
Larger Sample ◽  
Different Levels

Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven distinct tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

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