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

An analysis of field feedback in consumer electronics industry

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Big Data and Reliability Applications: The Complexity Dimension

Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. Meeker and Hong (2014, Quality Engineering, pp. 102-116) provided an extensive discussion of the opportunities and challenges in big data and reliability, and described engineering systems that can generate big data that can be used in reliability analysis. Meeker and Hong (2014) focused on large scale system operating and environment data (i.e., high-frequency multivariate time series data), and provided examples on how to link such data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. This paper intends to extend that discussion by focusing on how to use data with complicated structures to do reliability analysis. Such data types include high-dimensional sensor data, functional curve data, and image streams. We first provide a review of recent development in those directions, and then we provide a discussion on how analytical methods can be developed to tackle the challenging aspects that arise from the complexity feature of big data in reliability applications. The use of modern statistical methods such as variable selection, functional data analysis, scalar-on-image regression, spatio-temporal data models, and machine learning techniques will also be discussed.Comment: 28 pages, 7 figure

RELIABILITY AND MAINTAINABILITY IN INDUSTRY AND THE UNIVERSITIES

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76283/1/AIAA-1966-2598-214.pd

A General Approach to Electrical Vehicle Battery Remanufacturing System Design

One of the major difficulties electrical vehicle (EV) industry facing today is the production and lifetime cost of battery packs. Studies show that using remanufactured batteries can dramatically lower the cost. The major difference between remanufacturing and traditional manufacturing is the supply and demand variabilities and uncertainties differences. The returned core for remanufacturing operations (supply side) can vary considerably in terms of the time of returns and the quality of returned products. On the other hand, because different contracts can be used to regulate suppliers, it is almost always assumed zero uncertainty and variability for traditional manufacturing systems. Similarly, customers demand traditional manufacturers to sell newly produced products in constant high quality. But, remanufacturers usually sell in aftermarket, and the quality of the products demanded can vary depends on the price range, usage, customer segment and many other factors. The key is to match supply and demand side variabilities so the overlapping between them can be maximized. Because of these differences, a new framework is needed for remanufacturing system design. This research aims at developing a new approach to use remanufactured battery packs to fulfill EV warranties and customer aftermarket demands and to match supply and demand side variabilities. First, a market lifetime EV battery return (supply side) forecasting method is develop, and it is validated using Monte Carlo simulation. Second, a discrete event simulation method is developed to estimate EV battery lifetime cost for both customer and manufacturer/remanufacturer. Third, a new remanufacturing business model and a simulation framework are developed so both the quality and quantity aspects of supply and demand can be altered and the lifetime cost for both customer and manufacturer/remanufacturer can be minimized. The business models and methodologies developed in this dissertation provide managerial insights to benefit both the manufacturer/remanufacturer and customers in EV industry. Many findings and methodologies can also be readily used in other remanufacturing settings. The effectiveness of the proposed models is illustrated and validated by case studies.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143955/1/xrliang_1.pd

Predicting Cost/Reliability/Maintainability of Advanced General Aviation Avionics Equipment

A methodology is provided for assisting NASA in estimating the cost, reliability, and maintenance (CRM) requirements for general avionics equipment operating in the 1980's. Practical problems of predicting these factors are examined. The usefulness and short comings of different approaches for modeling coast and reliability estimates are discussed together with special problems caused by the lack of historical data on the cost of maintaining general aviation avionics. Suggestions are offered on how NASA might proceed in assessing cost reliability CRM implications in the absence of reliable generalized predictive models

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted

Prediction of remaining life of power transformers based on left truncated and right censored lifetime data

Prediction of the remaining life of high-voltage power transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Lifetime data for such transformers are complicated because transformer lifetimes can extend over many decades and transformer designs and manufacturing practices have evolved. We were asked to develop statistically-based predictions for the lifetimes of an energy company's fleet of high-voltage transmission and distribution transformers. The company's data records begin in 1980, providing information on installation and failure dates of transformers. Although the dataset contains many units that were installed before 1980, there is no information about units that were installed and failed before 1980. Thus, the data are left truncated and right censored. We use a parametric lifetime model to describe the lifetime distribution of individual transformers. We develop a statistical procedure, based on age-adjusted life distributions, for computing a prediction interval for remaining life for individual transformers now in service. We then extend these ideas to provide predictions and prediction intervals for the cumulative number of failures, over a range of time, for the overall fleet of transformers.Comment: Published in at http://dx.doi.org/10.1214/00-AOAS231 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org