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

Hazard rate models for early warranty issue detection using upstream supply chain information

This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier

Simulation of Automotive Warranty Data

This thesis will investigate the prediction of the number of claims in a two dimensional automotive warranty claim model for the case of minimal repair.The method involved fitting marginal distributions for age of claim and mileage of claim seperately. Next, various copulas were fitted to establish the correlation between age and mileage, and assessed for fit. The Gumbel copula is chosen as optimal. From this Gumbel copula, a simulation of warranty claims is undertaken. The method produced a good fit for claim age but performed less well for claim mileage, due to the asymmetry of the correlation between mileage and age. Further research directions to improve the accuracy and usefulness of this model are suggested

Deep Learning Based Reliability Models For High Dimensional Data

The reliability estimation of products has crucial applications in various industries, particularly in current competitive markets, as it has high economic impacts. Hence, reliability analysis and failure prediction are receiving increasing attention. Reliability models based on lifetime data have been developed for different modern applications. These models are able to predict failure by incorporating the influence of covariates on time-to-failure. The covariates are factors that affect the subjects’ lifetime. Modern technologies generate covariates which can be utilized to improve failure time prediction. However, there are several challenges to incorporate the covariates into reliability models. First, the covariates generally are high dimensional and topologically complex. Second, the existing reliability models are not efficient in modeling the effect on the complex covariates on failure time. Third, failure time information may not be available for all covariates, as collecting such information is a costly and time-consuming process. To overcome the first challenge, we propose a statistical approach to model the complex data. The proposed model generalizes penalized logistic regression to capture the spatial properties of the data. An efficient parameter estimation method is developed to make the model practical in case of large sample sizes. To tackle the second challenge, a deep learning-based reliability model is proposed. The model can capture the complex effect of the data on failure time. A novel loss function based on the partial likelihood function is developed to train the deep learning model. Furthermore, to overcome the third difficulty, we proposed a transfer learning-based reliability model to estimate failure time based on the failure time of similar covariates. The proposed model is based on a two-level autoencoder to minimize the distribution distance of covariates. A new parameter estimation method is developed to estimate the parameter of the proposed two-level autoencoder model. Various simulation studies are conducted to demonstrate the proposed models. The results show that the proposed models outperformed the traditional statistical and reliability models. Moreover, physical experiments on advanced high strength steel are designed to demonstrate the proposed model. As microstructure images of the steels affect the failure time of the steel, the images are considered as covariates. The results show that the proposed models predict the failure time and hazard function of the materials more accurately than existing reliability models

Työkalut luotettavuuden kehittämiseen tuotesuunnitteluprosessin aikana

One of the most in infuential factors when talking about industrial products are quality and reliability of the products. As company grows, grows also need for reliability plan. Purpose of this thesis was to present a variety of different reliability tools, and give examples of utilizing these tools. First tools revolved around Design for Reliability methodology. Its fundamental idea is to implement the reliability in the design process. Methods from the Design for Reliability consists of many different tools, while concentrating on preventive design with di erent analysis and simulations, and by using iterative design methods for testing, and redesign. Second set of tools was presented as a computer aided design. These tools takes advantage on developing computers, and digitalization, which can be utilized throughout the design process. Simulation for mechanical, electrical, and thermal phenomena can increase the reliability during the design process and in the meantime decrease time to market.Third part consists the important part for every design process, an analysis and selection of the components used. Errors on choosing the right component can have effects seen at long after the production process is over. It is important to know how to control the components reliability parameters, and also be aware the different ways of giving the reliability information. Also tolerances, parameter degradation and rating of components are discussed over the third part of this thesis. At each tool sets, the possible use is discussed during the presentation of the tools with the proper and right timed use. Also the possible impact the proper use of these tools might have to the new product development process is discussed

ESTIMATING THE RELIABILITY OF A NEW CONSUMER PRODUCT USING USER SURVEY DATA AND RELIABILITY TEST DATA

Because new products enter the market rapidly, estimating their reliability is challenging due to insufficient historical data. User survey data about similar devices (e.g., older versions of the new device) can be used as the prior information in a Bayesian analysis integrated with evidence in the form of product returns, reliability tests, and other reliability data sources to improve reliability estimation and test specification of the new product. User surveys are usually designed for purposes other than reliability estimation. Therefore, extracting reliability information from these surveys may be tricky or impossible. Even when possible, the extracted reliability information contains significant uncertainties. This dissertation introduces the critical elements of a reliability-informed user survey and offers methods for collecting them. A generic and flexible mathematical approach is then proposed. This approach uses the survey and reliability test data of similar products, for example, an older generation of the same product as prior knowledge. Then it combines them through a formal Bayesian analysis with the reliability test data to estimate the life distribution of the new product. The approach models continuous life distributions for products exposed to many damage-induced cycles. It proposes discrete life distribution models for products whose failures occur within several damaging cycles. The actual cycles for various applicable damaging stress profiles are converted into the equivalent (pseudo) cycles under a reference stress profile. When damage-induced cycles are estimated from user surveys, they may involve biases, as is the nature of most nontechnical users’ responses. This bias is minimized using an approach based on the Kullback-Leibler divergence method. The survey data and other evidence from similar products are then combined with the test data of the new product to estimate the parameters of the reliability model of the new product. The dissertation developed approaches to design reliability test specifications for a new product with unknown failure modes. The number of samples, stress levels, and the number of cycles for the accelerated life test are determined based on the manufacturer’s requirements, including the desired warranty time, the desired reliability with some confidence level at the warranty time, and the maximum number of samples. The actual use conditions (i.e., actual stress profiles and usage cycles) are grouped using clustering techniques. The centers of clusters are then used to design frequency-accelerated or stress-accelerated reliability tests. The application of the proposed reliability estimation approach and the test specification design approach is illustrated and used to validate the proposed algorithms using the simulated datasets for a hypothetical handheld electronic device with the failure mode of cracking caused by accidental drops. The proposed approaches can adequately estimate the reliability model and design test specifications for a wide range of consumer products. These approaches require reliability data about an existing product that is similar to the new product, however

Remanufacturing as a potential means of attaining sustainable industrial development in Indonesia

Remanufacturing industries account for a considerable share of small medium enterprises (SMEs) in both developed and developing countries. There is an urgent need for a sustainable manufacturing strategy for remanufacturing SMEs in developing countries in order for them to gain global market competitiveness through minimizing environmental impact while maximizing the economic and social benefits of SME manufacturing activities. This research uses Indonesian remanufacturing SMEs as a case study for sustainable manufacturing in developing countries

Data Mining in Automotive Warranty Analysis

This thesis is about data mining in automotive warranty analysis, with an emphasis on modeling the mean cumulative warranty cost or number of claims (per vehicle). In our study, we deal with a type of truncation that is typical for automotive warranty data, where the warranty coverage and the resulting warranty data are limited by age and mileage. Age, as a function of time, is known for all sold vehicles at all time. However, mileage is only observed for a vehicle with at least one claim and only at the time of the claim. To deal with this problem of incomplete mileage information, we consider a linear approach and a piece-wise linear approach within a nonparametric framework. We explore the univariate case, as well as the bivariate case. For the univariate case, we evaluate the mean cumulative warranty cost and its standard error as a function of age, a function of mileage, and a function of actual (calendar) time. For the bivariate case, we evaluate the mean cumulative warranty cost as a function of age and mileage. The effect of reporting delay of claim and several methods for making prediction are also considered. Throughout this thesis, we illustrate the ideas using examples based on real data

Reliability Abstracts and Technical Reviews January-December 1969

No abstract availabl

Air Force Institute of Technology Contributions to Air Force Research and Development, Calendar Year 1987

From the introduction:The primary mission of the Air Force Institute of Technology (AFIT) is education, but research and consulting are essential integral elements in the process. This report highlights AFIT\u27s contributions to Air Force research and development activities [in 1987]