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.

SYSTEM RELIABILITY GROWTH ANALYSIS USING COMPONENT FAILURE DATA

1994 ◽  
Vol 01 (01) ◽  
pp. 71-83 ◽  
Author(s):  
M. XIE ◽  
T.N. GOH
Keyword(s):  
System Reliability ◽  
Growth Analysis ◽  
Simple Approach ◽  
System Level ◽  
Reliability Growth ◽  
System Failure ◽  
Component Level ◽  
Component Failure ◽  
Failure Data ◽  
Growth Estimation

In this paper the problem of system-level reliability growth estimation using component-level failure data is studied. It is suggested that system failure data should be broken down into component, or subsystem, failure data when the above problems have occurred during the system testing phase. The proposed approach is especially useful when the system is not unchanged over the time, when some subsystems are improved more than others, or when the testing has been concentrated on different components at different time. These situations usually happen in practice and it may also be the case even if the system failure data is provided. Two sets of data are used to illustrate the simple approach; one is a set of component failure data for which all subsystems are available for testing at the same time and for the other set of data, the starting times are different for different subsystems.

Fault Diagnosis and Reliability Growth for Information System

2014 ◽  
Vol 590 ◽  
pp. 763-767
Author(s):  
Zhi Hui Huang
Keyword(s):  
Information System ◽  
Bayesian Network ◽  
System Reliability ◽  
Functional Module ◽  
Decision Problems ◽  
Reliability Growth ◽  
Failure Data ◽  
Manufacturing Execution Systems ◽  
Growth Planning ◽  
Manufacturing Execution

This paper aiming at the zero-failure data and uncertain-decision problems exist in the information system reliability growth process, it proposes to build the Bayesian network topology of FMEA. It adopts Leaky Noisy-OR model, and it analyses the probability that the subsystem functional module will go wrong in quantity. It solves the problem of identifying the vague and incomplete information exists in the complex system rapidly and accurately, laying the foundation for further study of the reliability growth comprehensive ability assessment of system based on the Bayesian network. In this paper, on the background of Manufacturing Execution Systems (MES) engineering, aimed at research on models and evaluation methods of reliability growth for MES, enclosing reliability of MES task and design target, reliability growth test and analysis methods, it proposes the goal of MES reliability growth planning.

ML ESTIMATES FOR CROW/AMSAA RELIABILITY GROWTH MODEL FOR GROUPED AND MIXED TYPES OF SOFTWARE FAILURE DATA

2004 ◽  
Vol 11 (04) ◽  
pp. 329-337
Author(s):  
RANI ◽  
R. B. MISRA
Keyword(s):  
Growth Models ◽  
Quantitative Measure ◽  
Interval Data ◽  
Comprehensive Treatment ◽  
Reliability Growth ◽  
Data Types ◽  
Software Failure ◽  
Failure Data ◽  
Stochastic Basis ◽  
Hybrid Data

A number of software reliability growth models have been proposed into the literature for estimating reliability during software testing. Duane's model,7 originally proposed for hardware reliability is also used in estimating reliability of the software during development testing. Graphical interpretation of Duane's postulate subsequently was given a concrete stochastic basis by Crow,3 and provided a comprehensive treatment of this model in the context of reliability growth and demonstrated its elegant inferential aspects. Parameters of the Crow model have physical interpretation and can yield quantitative measure for reliability growth assessment. This paper proposes a simple and efficient procedure to determine parameters of Crow/AMSAA model using one dimensional bisection method for grouped/interval data, where failures are recorded at various time points. In addition this paper proposes a method to estimate parameters when there exist a mixture of grouped and individual (mixed or hybrid) data types. Proposed method's application is illustrated with numerical examples using both simulated and real software failure data.

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.

Markovian Availability Measurement and Assessement for Hardware-Software System

1997 ◽  
Vol 04 (03) ◽  
pp. 257-268 ◽  
Author(s):  
Koichi Tokuno ◽  
Shigeru Yamada
Keyword(s):  
System Reliability ◽  
Software Systems ◽  
Software System ◽  
Reliability Growth ◽  
System Availability ◽  
Software Failures ◽  
Software Failure ◽  
Dependent Behavior ◽  
Reliability Performance ◽  
Availability Model

It is important to take into account the trade-off between hardware and software systems when total computer-system reliability/performance are evaluated and assessed. We develop an availability model for a hardware-software system. The system treated here consists of one hardware subsystem and one software subsystem and it is assumed that the system is down and restored whenever a hardware or a software failure occurs. Especially, for the software subsystem, it is supposed that (i) the restoration actions are not always performed perfectly, (ii) the restoration times for later software failures become longer and (iii) reliability growth occurs in the perfect restoration action. The hardware and the software failure-occurrence phenomena are respectively described by constant and geometrically decreasing hazard rates. The time-dependent behavior of the system, which alternately repeats the operational state that a system is operating without failures and the restoration state that a system is inoperable and restored, is described by a Markov process. Useful expressions for several quantitative measures of system performance are derived from this model. Finally, numerical examples are presented for illustration of system availability measurement and assessment.

An Adaptive EM Algorithm for the Maximum Likelihood Estimation of Non-Homogeneous Poisson Process Software Reliability Growth Models

2017 ◽  
Vol 24 (04) ◽  
pp. 1750020 ◽  
Author(s):  
Vidhyashree Nagaraju ◽  
Lance Fiondella ◽  
Panlop Zeephongsekul ◽  
Thierry Wandji
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Growth ◽  
Homogeneous Poisson Process ◽  
Em Algorithms ◽  
Failure Data ◽  
Software Reliability Growth ◽  
Non Homogeneous Poisson Process

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGM a ) enable quantitative metrics to guide decisions during the software engineering life cycle, including test resource allocation and release planning. However, many SRGM possess complex mathematical forms that make them difficult to apply. Specifically, traditional procedures solve a system of nonlinear equations to identify the numerical parameters that best characterize failure data. Recently, researchers have developed expectation-maximization (EM) algorithms for NHPP SRGM that exhibit better convergence properties and can therefore find maximum likelihood estimates with greater ease. This paper presents an adaptive EM (AEM) algorithm, which combines an earlier EM algorithm for NHPP SRGM with unconstrained search of the model parameter space. Our performance analysis shows that the AEM outperforms state-of-the-art EM algorithms for NHPP SRGM with very strong statistical significance, which is as much as hundreds of times faster on some data sets. Thus, the approach can fit SRGM very quickly. We also incorporate this high performance adaptive EM algorithm into a heuristic nested model selection procedure to objectively select a model of least complexity that best characterizes the failure data. Results indicate this heuristic approach often identifies the model possessing the best model selection criteria. a Acronyms are not pluralized.

Reliability Prediction Based on Field Failure Data of Guided Missile

2018 ◽  
Vol 18 (3) ◽  
pp. 250-259 ◽  
Author(s):  
Yangwoo Seo ◽  
Kyeshin Lee ◽  
Younho Lee ◽  
Jeyong Kim
Keyword(s):  
Reliability Prediction ◽  
Failure Data ◽  
Field Failure
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