System Reliability Growth Analysis during Warranty

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.

Research of Reliability Growth Evaluation of CNC Machine Tools Based on Weibull Distribution

In light of the characteristics and small samples of the research of the CNC machine tools based on Weibull distributions，dynamic modeling and middle rank are used to evaluate the failure rate of each test phase. Traditional Duane model are referred to fit parameters of the reliability growth model with least squares.

Research on Mechanical Reliability Growth Model for Complete Failure Data

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.

Superposition of Power-Law Models for Hardware/Software System Reliability Data Analysis

One of the widely used NHPP models in reliability is the so-called power-law model, also known as the Duane model. A power-law model can be applied in analyzing failure data of both software and hardware systems. Nevertheless, the power-law model is no longer applicable to describe the failure behavior when a hardware/software embedded system is concerned since the failures can come from both hardware and software. How to analyze the failure data of this type is still a problem to study and a new type of model is needed to develop. In this paper, we consider the superposition of the power-law models (SPLM) as one candidate to describe the failure behaviors of hardware/software systems. The characteristics of SPLMs are thoroughly studied. It is shown that an SPLM has the intensity function that can be increasing, decreasing, increasing-then-decreasing or decreasing-then-increasing. Specifically, the identification method of the superposition of power-law processes is provided by using the TTT-plot technique. The TTT-transform of a superposition of power-law processes is DFR-like. Therefore, the conditional TTT-plot should present a convex pattern if the system failures follow a superposition process by a few power-law processes. This provides us an easy way to identify the model when the testing data is available.

New Results on Goodness-of-Fit Tests for the Power-Law Process and Application to Software Reliability

The Power-Law process, also known as the Duane model, is a nonhomogeneous Poisson process which is widely used in reliability growth modeling. Goodness-of-fit tests for this process have been proposed by Crow, Rigdon, Klefsjö–Kumar and Park–Kim. In this paper, we propose several new tests based on chi-square, Laplace and conditional probability integral transform (CPIT) methodologies. We show that the TTT-plot test reduces to the Laplace test with Durbin's modification. We compare the power of 15 goodness-of-fit tests for the Power-Law process, and apply them to real software reliability data.