Research on Mechanical Reliability Growth Model for Complete Failure Data

2010 ◽  
Vol 118-120 ◽  
pp. 536-540 ◽  
Author(s):  
Zhi Li Sun ◽  
Yu Guo ◽  
Shi Ji
Keyword(s):  
Growth Model ◽  
Small Sample ◽  
Reliability Growth ◽  
Mechanical Reliability ◽  
Failure Data ◽  
Mechanical Products ◽  
Duane Model ◽  
Complete Failure ◽  
Growth Technology ◽  
Mechanical Characters

As everyone knows, reliability growth technology is an essential part in the mechanical reliability theory as well as an insurance of the products capability in usage. It exists throughout the entire lifespan of development, manufacturing and application. Concerning the reliability characters of mechanical products, that product life obeys Weibull distribution, which is mostly resulted from the test on the small sample, three parameters of life distribution are estimated by the grey estimation in this paper. Then according to the data acquired in the test, Duane growth model is surely developed to assess the situation of reliability growth. Furthermore, the following example ascertains that the developed model is in accordance with mechanical characters. From the result, Duane model is reasonable to evaluate the reliability growth level of mechanical products. It is obvious that the improved measure is effective to enhance the reliability and the value of MTBF can be calculated with the model.

Small sample discrete reliability growth modeling using a grey systems model

2018 ◽  
Vol 8 (3) ◽  
pp. 246-271 ◽  
Author(s):  
Thomas Paul Talafuse ◽  
Edward A. Pohl
Keyword(s):  
Growth Model ◽  
Small Sample ◽  
System Level ◽  
Parameter Estimates ◽  
Reliability Growth ◽  
Sample Sizes ◽  
Content Type ◽  
Failure Data ◽  
Amsaa Model ◽  
Small Sample Sizes

PurposeWhen performing system-level developmental testing, time and expenses generally warrant a small sample size for failure data. Upon failure discovery, redesigns and/or corrective actions can be implemented to improve system reliability. Current methods for estimating discrete (one-shot) reliability growth, namely the Crow (AMSAA) growth model, stipulate that parameter estimates have a great level of uncertainty when dealing with small sample sizes. The purpose of this paper is to present an application of a modified GM(1,1) model for handling system-level testing constrained by small sample sizes.Design/methodology/approachThe paper presents a methodology for incorporating failure data into a modified GM(1,1) model for systems with failures following a poly-Weibull distribution. Notional failure data are generated for complex systems and characterization of reliability growth parameters is performed via both the traditional AMSAA model and the GM(1,1) model for purposes of comparing and assessing performance.FindingsThe modified GM(1,1) model requires less complex computational effort and provides a more accurate prediction of reliability growth model parameters for small sample sizes and multiple failure modes when compared to the AMSAA model. It is especially superior to the AMSAA model in later stages of testing.Originality/valueThis research identifies cost-effective methods for developing more accurate reliability growth parameter estimates than those currently used.

AN NHPP SOFTWARE RELIABILITY GROWTH MODEL WITH IMPERFECT DEBUGGING AND ERROR GENERATION

2014 ◽  
Vol 21 (02) ◽  
pp. 1450008 ◽  
Author(s):  
P. ROY ◽  
G. S. MAHAPATRA ◽  
K. N. DEY
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Data Sets ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Failure Data ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Error Generation

In this paper, we propose a non-homogeneous Poisson process (NHPP) based software reliability growth model (SRGM) in the presence of modified imperfect debugging and fault generation phenomenon. The testing team may not be able to remove a fault perfectly on observation of a failure due to the complexity of software systems and incomplete understanding of software, and the original fault may remain, or get replaced by another fault causing error generation. We have proposed an exponentially increasing fault content function and constant fault detection rate. The total fault content of the software for our proposed model increases rapidly at the beginning of the testing process. It grows gradually at the end of the testing process because of increasing efficiency of the testing team with testing time. We use the maximum likelihood estimation method to estimate the unknown parameters of the proposed model. The applicability of our proposed model and comparisons with established models in terms of goodness of fit and predictive validity have been presented using five known software failure data sets. Experimental results show that the proposed model gives a better fit to the real failure data sets and predicts the future behavior of software development more accurately than the traditional SRGMs.

scholarly journals System Reliability Growth Analysis during Warranty

2019 ◽  
Vol 4 (1) ◽  
pp. 85-94 ◽  
Author(s):  
James Li ◽  
Greg Collins ◽  
Ravi Govindarajulu
Keyword(s):  
System Reliability ◽  
Growth Analysis ◽  
Growth Models ◽  
Primary Objective ◽  
Reliability Growth ◽  
Failure Data ◽  
Duane Model ◽  
Actual Field ◽  
Reliability Performance ◽  
Field Failure

This paper presents system reliability growth analysis using actual field failure data. The primary objective of the system reliability growth is to improve the achievement of system reliability performance during system reliability demonstration, in order to achieve the predicted or contractually required system reliability commitment. An effective reliability growth model can be utilized to predict when the reliability target can be achieved based on previous reliability performance. In this paper, the system reliability growth analysis is illustrated using the Duane and AMSAA reliability growth models to determine applicability and aid in choice determination. The Duane model is a better choice for failure terminated reliability growth while AMSAA is a better choice for time terminated reliability growth. Comparisons of the Duane versus AMSAA model are carried out by conducting the statistical analysis on the observed field failures.

scholarly journals A generalized Gompertz growth model with applications and related birth-death processes

2020 ◽  
Author(s):  
Majid Asadi ◽  
Antonio Di Crescenzo ◽  
Farkhondeh A. Sajadi ◽  
Serena Spina
Keyword(s):  
Growth Model ◽  
Growth Curve ◽  
Goodness Of Fit ◽  
Gompertz Model ◽  
Death Process ◽  
Birth Process ◽  
Failure Data ◽  
First Case ◽  
Gompertz Growth Model ◽  
Birth Death

AbstractIn this paper, we propose a flexible growth model that constitutes a suitable generalization of the well-known Gompertz model. We perform an analysis of various features of interest, including a sensitivity analysis of the initial value and the three parameters of the model. We show that the considered model provides a good fit to some real datasets concerning the growth of the number of individuals infected during the COVID-19 outbreak, and software failure data. The goodness of fit is established on the ground of the ISRP metric and the $$d_2$$ d 2 -distance. We also analyze two time-inhomogeneous stochastic processes, namely a birth-death process and a birth process, whose means are equal to the proposed growth curve. In the first case we obtain the probability of ultimate extinction, being 0 an absorbing endpoint. We also deal with a threshold crossing problem both for the proposed growth curve and the corresponding birth process. A simulation procedure for the latter process is also exploited.

A reliability growth model for 300 MW pumped-storage power units

2009 ◽  
Vol 3 (3) ◽  
pp. 337-340 ◽  
Author(s):  
Jinyuan Shi ◽  
Yu Yang ◽  
Zhicheng Deng
Keyword(s):  
Growth Model ◽  
Reliability Growth ◽  
Pumped Storage
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