Warranty Data Analysis: A Review

Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA

Designing Advanced Reliability Testing Mathematical Model for Modern Products

The modern era is the age of science, technology and at the same time it is the age of competition. The advancement of new technology and increased global competition have emphasized the importance of product strength and reliability estimation. As a result, producers and manufacturers must now verify the strength and reliability of their products prior to releasing them to the market. In the past, reliability data analysis was a critical tool for this purpose. Traditionally, reliability data analysis entails quantifying these life characteristics through the examination of failure data. However, in many situations, obtaining such failure data has been extremely difficult, if not impossible, due to the length of time between designing and releasing a product, and the difficulty of designing a product that will last a long period due to its continuous use and operation. Faced with this challenge, reliability statisticians developed a technique called Accelerated Reliability Testing to rapidly determine the reliability and life characteristics of products. This technique increases product reliability and identifies when and how a product will fail in its intended environment. In the present work, we plan to investigate these mathematical reliability models to determine the costs associated with the various product guarantees. If component lifetimes follow the power-function distribution, the problem is examined under increasing stress using percent failure censoring. The method is referred as a process that applies accelerated testing to estimate the cost of age-replacement for goods sold under warranty. Additionally, a mathematical illustration is presented to illustrate the results

Reliability Analysis of Photovoltaic Systems for Specific Applications

This contribution is dedicated to the analysis of a reliable PV system for specific applications. The reliability study was based on: (1) the RAMS (Reliability, Availability, Maintenance, and Safety) model applied to a PV system by using a simulation SYNTHESIS platform developed by ReliaSoft, and (2) the simulation of the PV system using the SYNTHESIS platform and TM-21 Calculator software developed by ENERGY STAR. The objective of this analysis was to obtain a more stable and long-lasting operation of a PV system regarding reliability, maintainability, availability and degradation of the system

On the Statistical Modeling and Analysis of Repairable Systems

We review basic modeling approaches for failure and maintenance data from repairable systems. In particular we consider imperfect repair models, defined in terms of virtual age processes, and the trend-renewal process which extends the nonhomogeneous Poisson process and the renewal process. In the case where several systems of the same kind are observed, we show how observed covariates and unobserved heterogeneity can be included in the models. We also consider various approaches to trend testing. Modern reliability data bases usually contain information on the type of failure, the type of maintenance and so forth in addition to the failure times themselves. Basing our work on recent literature we present a framework where the observed events are modeled as marked point processes, with marks labeling the types of events. Throughout the paper the emphasis is more on modeling than on statistical inference.Comment: Published at http://dx.doi.org/10.1214/088342306000000448 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Engineering applications of Bayesian statistical methods

This dissertation makes Bayesian contributions to engineering statistics in three basic areas. These are methods for combining information, modeling repairable system reliability, and designing experiments.;A recursive Bayesian hierarchical model (RBHM) is presented. An RBHM can be used to combine information from physical data, data from a computer model of a process, and experts. In an example involving a fluidized bed process, an RBHM is used to estimate location and scale biases of one source of information for another.;The need to document the reliability of the Blue Mountain supercomputer motivates the work on system reliability. A detailed reliability analysis of this supercomputer is presented, using a Bayesian hierarchical nonhomogeneous Poisson process model. Further, some flexible new families of intensities for nonhomogeneous Poisson processes are defined and Bayes inference for them is discussed.;Finally, the problem of estimating expected information gain for planned data collection is considered. Two methods of estimation are applied to the so called random fatigue-limit model, a 5 parameter model important in some materials engineering applications

Planning and inference of sequential accelerated life tests

Ph.DDOCTOR OF PHILOSOPH

Statistical Reliability with Applications

This chapter reviews fundamental ideas in reliability theory and inference. The first part of the chapter accounts for lifetime distributions that are used in engineering reliability analyis, including general properties of reliability distributions that pertain to lifetime for manufactured products. Certain distributions are formulated on the basis of simple physical properties, and other are more or less empirical. The first part of the chapter ends with a description of graphical and analytical methods to find appropriate lifetime distributions for a set of failure data. The second part of the chapter describes statistical methods for analyzing reliability data, including maximum likelihood estimation and likelihood ratio testing. Degradation data are more prevalent in experiments in which failure is rare and test time is limited. Special regression techniques for degradation data can be used to draw inference on the underlying lifetime distribution, even if failures are rarely observed. The last part of the chapter discusses reliability for systems. Along with the components that comprise the system, reliability analysis must take account of the system configuration and (stochastic) component dependencies. System reliability is illustrated with an analysis of logistics systems (e.g., moving goods in a system of product sources and retail outlets). Robust reliability design can be used to construct a supply chain that runs with maximum efficiency or minimum cost