scholarly journals SoRel: A tool for reliability growth analysis and prediction from statistical failure data

2002 ◽  
Author(s):  
K. Kanoun ◽  
M. Kaaniche ◽  
J.-C. Laprie ◽  
S. Metge
Keyword(s):  
Growth Analysis ◽  
Reliability Growth ◽  
Failure Data

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.

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.

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.

A SOFTWARE RELIABILITY MODEL FOR ARTIFICIAL INTELLIGENCE PROGRAMS

1993 ◽  
Vol 03 (01) ◽  
pp. 99-114 ◽  
Author(s):  
FAROKH B. BASTANI ◽  
ING-RAY CHEN ◽  
TA-WEI TSAO
Keyword(s):  
Artificial Intelligence ◽  
Software Reliability ◽  
Planning Problem ◽  
Reliability Growth ◽  
Reliability Model ◽  
Reliability Models ◽  
Failure Data ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Correctness Criterion

In this paper we develop a software reliability model for Artificial Intelligence (AI) programs. We show that conventional software reliability models must be modified to incorporate certain special characteristics of AI programs, such as (1) failures due to intrinsic faults, e.g., limitations due to heuristics and other basic AI techniques, (2) fuzzy correctness criterion, i.e., difficulty in accurately classifying the output of some AI programs as correct or incorrect, (3) planning-time versus execution-time tradeoffs, and (4) reliability growth due to an evolving knowledge base. We illustrate the approach by modifying the Musa-Okumoto software reliability growth model to incorporate failures due to intrinsic faults and to accept fuzzy failure data. The utility of the model is exemplified with a robot path-planning problem.

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.

Debugging-workflow-aware software reliability growth analysis

2017 ◽  
Vol 27 (7) ◽  
pp. e1638 ◽  
Author(s):  
Marcello Cinque ◽  
Domenico Cotroneo ◽  
Antonio Pecchia ◽  
Roberto Pietrantuono ◽  
Stefano Russo
Keyword(s):  
Software Reliability ◽  
Growth Analysis ◽  
Reliability Growth ◽  
Software Reliability Growth

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.

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