AMSAA Model Research Articles

A highly accelerated stress reliability growth test method

AbstractReliability growth test (RGT) is one of the main methods to improve product reliability. In order to effectively solve the problem of short development cycles faced by enterprises, the accelerated reliability growth test (ARGT) is developed. Since the traditional ARGT is carried out separately under all of the accelerated stress levels, and the time of RGT is too long under low accelerated stress levels. So, a highly accelerated stress reliability growth test (HARGT) is proposed, where the RGT is designed to carry out at the highest accelerated stress level. Firstly, AMSAA model is adopted to track the reliability growth process under the highest accelerated stress level, and its parameters are determined by the maximum likelihood estimation method. Secondly, taking temperature as the accelerated stress, the relationship of the reliability with different accelerated stress levels is established under the accelerated life test (ALT) in the first stage. Based on the Arrhenius model and combined with the acceleration coefficient, the product reliability at the end of the test is extrapolated to the product reliability at the normal stress level. The parameters of the Arrhenius model are got through the least square estimation method, and the acceleration coefficient and equation are also obtained. Finally, a case study on the communication service system is given to show the effectiveness of the proposed method.

Discrete Dynamic Modeling and Change Trend Analysis of Regional Economy Based on Big Data

The driving force of high-quality development of regional economy is inseparable from the support of technology. With the support of big data, we need to solve this problem in order to solve the difficulty of large-scale experimental testing and accurately reflect the feasibility growth of data sample changes. This paper proposes a discrete dynamic modeling technology based on big data background to analyze the development and change of regional economy. The reliability AMSAA model is usually used for dynamic discrete modeling. It can be combined with the change data provided by big data to form a dynamic modeling method for reliability growth evaluation. Then, the Bayesian regression method is used to predict the change parameters of the model, and the spatial econometric method is used to analyze the regional economic change. The results show that compared with the traditional methods, the discrete dynamic modeling method is more accurate and can effectively solve the problem of reliable growth under the condition of big data. After introducing the spatial effect measurement model, it can also reflect the main factors of the growth and change of regional economic real output value. In addition to the development of high and new technology, terrain factors, investment, and government support have also had different effects. Therefore, according to the above results, it is proved that the discrete dynamic modeling technology can accurately obtain the experimental data and provide reliable technical support for dynamic data processing.

Using Amsaa Model in Evaluating Reliability Growth Testing

Reliability growth is defined as the positive progress in reliability over a period of time according to changes in product design or manufacturing processes. There are three main areas of reliability growth: planning, tracking and projecting. The most important model is the tracking models The model parameters were estimated using the maximum potential method as well as estimation of the confidence intervals for these parameters, estimation of the confidence intervals for the time-to-time ratio (DMTBF), and finally the model was simulated using the Monte Carlo method using specialized software (RGA)

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.

Small sample discrete reliability growth modeling using a grey systems model

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.

Robust allocation of testing resources in reliability growth

Reliability growth testing seeks to improve system reliability by identifying and removing failure modes. Recent models maximize system reliability by allocating limited testing resources across the system’s components, each of which exhibits reliability growth according to the AMSAA model (Crow, 1974) with known parameters. We extend this research to solve a robust version of this problem for both series and series-parallel systems in which AMSAA parameters are uncertain but assumed to lie within a budget-restricted uncertainty set. We develop and analyze an exact solution approach for this problem based on a cutting-plane algorithm. In the case of series systems, we demonstrate that the subproblems from this algorithm are efficiently solvable via dynamic programming. Computational results demonstrate (i) the value of the robust optimization approach as compared to deterministic alternatives and (ii) the efficacy of our algorithm.

Small sample reliability growth modeling using a grey systems model

ABSTRACTWhen 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 reliability growth, namely the Crow (AMSAA) growth model, stipulate that parameter estimates have a great level of uncertainty when dealing with small sample sizes. For purposes of handling limited failure data, we propose the use of a modified GM(1,1) model to predict system reliability growth parameters and investigate how parameter estimates are affected by systems whose failures follow a poly-Weibull distribution. Monte-Carlo simulation is used to map the response surface of system reliability, and results are used to compare the accuracy of the modified GM(1,1) model to that of the AMSAA growth model. It is shown that with small sample sizes and multiple failure modes, the modified GM(1,1) model is more accurate than the AMSAA model for prediction of growth model parameters.

Research on Complex Equipment Reliability Growth AMSAA-ELP Model Based on Explore Learning Promotion

According to learning other models equipment test information of complex equipment in the development process, and making their own systems to improve the reliability of the case, a complex equipment reliability growth AMSAA-ELP model based on explore learning promotion was developed, and the property was analyzed from different parameter values. Additionally, the trend test was also presented. Secondly, maximum likelihood estimation formula of the parameters was given under time censored and failure censored test of AMSAA-ELP model, and point out that there are multiple poles value of the maximum likelihood estimate can use pseudo-Monte-Carlo method parameter calculation. Additionally, the model's goodness of fit test was also given. Finally, the combination of complex equipment with engine failure data was analyzed. The results shows that the AMSAA-ELP model is prefer to AMSAA model intended to test data, and the AMSAA-ELP model is suitable to the engineering applications.

Reliability Analysis Based on Improved Bayes Method of AMSAA Model

Aiming at the defects that failure samples of five-axis NC machine tools is small, traditional reliability analysis is not accurate, this paper presents reliability analysis mode based on improved Bayesian method for AMSAA model. Firstly, we obtain the failure model of NC machine tools meets the AMSAA model according to goodness-of-fit test, and in order to meet the requirements of simplifying engineering calculations, this paper adpots a method of Coefficient equivalent which converts failure Data into index-life data; then using Bayesian methods to estimate reliability parameters for the Index-life data; for the last we proceed point estimation and interval estimation for the MTBF of the machine. Take High-speed five-axis NC machine tools t of VMC650m for example, the result proved that the method can take advantage of a small sample of the equipment to proceed point estimation and interval estimation for MTBF failure data, and provide a reference for the optimization of maintenance strategies and Diagnostic work of the NC machine tools.

Research on AMSAA-BISE model—An answer to Mei Men-Hua

The AMSAA-BISE model is derived from another approach. This certainly shows the correctness of the AMSAA-BISE model, and indicates the incorrectness of the approximate model given in this paper. The engineering example illustrating these conclusions is given. Merits and demerits of AMSAA and AMSAA-BISE model are discussed.

A New Nonparametric Growth Model

This paper proposes a new nonparametric reliability growth model for the analysis of the failure rate of a system that is undergoing development test. The only restrictions on the actual, unknown failure distribution for each stage of testing is that it be continuous, have only one unknown parameter θ, and have an associated unimodal likelihood function. No assumptions regarding the parametric form of the failure rate of the development process are made, only that there is no decay in the reliability of the system during the design changes. The parameters are assumed to be ordered from one test stage to the next such that θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ⩾ θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ⩾ ... ⩾ θm. The new model performs very well based on relative error and mean square error. The model is generally superior to the popular AMSAA model, regardless of the actual underlying failure process. In addition, the results indicate a notable bias in the AMSAA model, early in the development process, regardless of the actual underlying failure process.

Reliability growth for multi-system simultaneous development

In this paper the AMSAA model for multi-system simultaneous development is discussed. The maximum likelihood (ML) estimates of the parameters and the confidence intervals of the MTBF for Weibull process are presented. Crow's formulae[1] are the special examples for this paper.