bluuebunny/crossref_metadata_2025_split huggingface dataset:
Process Capability Indices for Multiple Stream Processes
Authors: Bothe Davis Ross 
Published: September 2014

Abstract

Multiple streams are created in a manufacturing process when its flow of parts is divided into several supposedly identical paths. Presumably, the quality of parts produced by each stream is identical but, in practice, there are usually differences between streams. The capability metric introduced in this article, called the
average C

pk

index
, is used when a practitioner desires a measure of capability for the combined output of all streams. Because this new index is derived from the average percentage of nonconforming parts expected in the mixed output of all streams, it works equally well with both variable‐type data (having either unilateral or bilateral specifications) and attribute‐type data. Two examples are provided: the hole size of parts produced by a four‐cavity mold in an injection‐molding operation; and the rejection rate of a food product made on six lines.
Measurement Systems Analysis, Attribute
Authors: Borror Connie M. 
Published: September 2014

Abstract
Methods for assessing the capability of a quantitative or continuous measurement system are well documented and include the tabular method and the analysis of variance approach. When the measurement system involves attribute data, the standard quantitative methods are not appropriate. An attribute gauge measurement system is one where the parts or objects under study are placed into one of two or more possible categories. The classification of the part is the measurement of interest in an attribute measurement system. Several commonly used statistics for “appraiser agreement” and attribute gauge repeatability and reproducibility are presented.
Warranty: Usage and Wear Process for
Authors: Wilson Simon P. 
Published: September 2014

AbstractMany products, for which warranties are offered, are subject to wear and this can be a major factor in when the product fails. The process by which wear accumulates is best modeled as a stochastic process. In this article, I describe some stochastic processes for wear. Each process is defined, the ease with which its probability distribution can be computed is described, and its advantages and disadvantages as a wear process is discussed. I briefly discuss how these models may be applied to practical warranty problems such as for products whose warranty is defined in terms of time and some measure of usage (like kilometers traveled) to failure.
Warranty Modeling
Authors: Thomas Marlin U. 
Published: September 2014

Abstract
Manufacturer warranty costs vary with product quality, particularly reliability and failure characteristics that relate to warranty claims. These costs represent a significant portion of the total cost and are therefore important in product planning and manufacturing control decisions. In this article, models are presented for assisting manufacturers in analyzing and making economic decisions for product warranties. Methods are developed for predicting costs, determining the amount of reserve funds for covering warranty expenses, and determining optimum periods of warranty coverage.
Total Time on Test Plots
Authors: Bergman Bo , Klefsjö B. 
Published: September 2014

AbstractIn the 1970s the total time on test (TTT) plot, and its theoretical counterpart, the scaled TTT transform, was presented by R. E. Barlowet al. as a tool for model identification based on data representing lives of nonrepairable equipment. It has been used when analyzing different aging properties, optimising replacement policies and studying test statistics. Generalisations of the TTT plot have also been used for analysis of data from repairable units. It has also been suggested as a tool for the selection of active factors in design of experiments. In this paper we give an overview of TTT plotting and its practical and theoretical applications. A wide range of references are suggested for further reading.
Maintenance Optimization in Random Environments
Authors: Özekici Süleyman 
Published: September 2014

Abstract
We consider reliability models that operate in a randomly changing environment. The random environment represents important factors that affect the stochastic and deterministic parameters of the reliability model under consideration. We focus, in particular, on the maintenance problem involving complex systems. We do not aim to report new results on recent ongoing research. Rather, the objective is to provide an overview of the models and results on reliability models in random environment. This will include mainly previous work done by the author.
Group Maintenance Policies
Authors: Wilson John G. , Benmerzouga Ali , Anderson Chris K. 
Published: September 2014

Abstract

This paper introduces a new group replacement policy for
n
independent identical machines operating in parallel. This policy is a generalization of the classes of
m
‐failure, (
m
,
T
) and
T
‐failure policies. The adaptive nature of this policy is its most attractive feature: the replacement rule varies according to the state of the system. Once the rule has been found, implementation is no harder than for other standard policies which do not incorporate this adaptive feature. An attractive feature of this approach is that no other policy that uses only the times of operations of the machines and the number of machines that have failed can do better. It is demonstrated that the classes of
m
‐failure, (
m
,
T
) and
T
‐failure policies are not generally the optimal.
Expert Opinion in Reliability
Authors: Sims Benjamin H. , Wiedlea Andrew Koehler , Wilson Gregory D. 
Published: September 2014

AbstractExpert opinion can be a useful source of information for assessing reliability, provided it is obtained in a manner that minimizes common sources of bias and ambiguity. The term for obtaining expert opinion in this manner iselicitation. In reliability assessment, elicitation is most often directed at two distinct elements of expert opinion: expertjudgmentabout the value of a specific quantity or probability of interest, or expertknowledgeabout the dependency structure of a system.
Multivariate Age and Multivariate Renewal Replacement
Authors: Ebrahimi Nader 
Published: September 2014

Abstract

A large part of reliability theory deals with replacement of single‐component systems. Two of the popular replacement policies are (a) age‐replacement and (b) block‐replacement policies. As systems become more complicated and require new technologies and methodologies, more sophisticated maintenance policies are needed to solve the maintenance problems. For systems consisting of
k
related components, a legitimate question is when to replace the components. Obviously, the classical age‐and block‐replacement policies are unsuitable for such situations. This article deals with multivariate versions of age replacement policies that may be used for replacing components. Also, for planning purposes, the article describes several optimal age replacement policies.
System Availability
Authors: Gaver Donald P. 
Published: September 2014

Abstract

Availability
quantifies the propensity of a system to be functionally operative upon demand. It increases if operating times between failures (“uptimes”) are long, and decreases if, following failure or anticipatory removal, logistics delays and repair (“downtimes”) are protracted. This chapter summarizes the general availability concept and discusses the limitation of
operational availability
suggesting that
mission availability
is often more useful and appropriate.
Global and Dynamic Information Measures for Reliability
Authors: Ebrahimi Nader , Soofi Ehsan S. 
Published: September 2014

Abstract
In modeling a duration variable, such as the lifetime of an object as it ages, consideration of the age partitions the support of the lifetime distribution progressively and induces truncated distributions for the past and residual lifetimes. Global information measures of the distribution of the entire lifetime do not change with the age, and thus are static. Information measures of the past and residual lifetime distributions are functions of the age, and hence are dynamic. This article presents an overview of the global and dynamic Shannon entropy, Kullback–Leibler information, and mutual information. Examples include information measures for parallel and series systems, proportional hazard, exponential, and Pareto models.
Nonparametric and SemiparametricBayesian Reliability Analysis
Authors: Ghosh Kaushik , Tiwari Ram C. 
Published: September 2014

AbstractIn this article, we first provide an overview of some nonparametric Bayesian methods of inference using life‐history data. These methods include those that use the Dirichlet process, the gamma process, and the beta process as the prior. We then present a semiparametric Bayesian method for estimating the reliability of a component in a multicomponent system using lifetime data from several systems. This method assumes that the component lifetimes have a parametric distribution with a Dirichlet process prior on the distribution of the parameters.
Multicomponent Maintenance
Authors: Nicolai Robin P. , Dekker Rommert 
Published: September 2014

Abstract
In the past few decades the maintenance of multicomponent systems has become more and more complex and, as a consequence, it has got the attention of many scholars. Multicomponent maintenance is the problem of finding optimal maintenance policies for a system consisting of several units of machines or many pieces of equipment, which may or may not depend on each other. Generally, three types of interactions or dependencies can be distinguished: economic dependence, stochastic dependence, and structural dependence. Economic dependence implies that grouping maintenance actions either saves costs (e.g. because to economies of scale) or yields higher costs (e.g. because of manpower restrictions) compared to multiple individual maintenance actions. Stochastic dependence occurs if the condition of the components influences the lifetime distribution of other components. Structural dependence applies if components structurally form a part, so that maintenance of a failed component implies maintenance of working components as well.
On the basis of the planning aspect, we may distinguish between two types of models for multicomponent maintenance: stationary models and dynamic models. In stationary models, a long‐term stable situation is assumed and mostly these models assume an infinite planning horizon. In dynamic models, short‐term information can be taken into account. These models generate dynamic decisions that may change over the planning horizon.
M
arkov Renewal Processes in Reliability Modeling
Authors: Çekyay Bora , Özekici Süleyman 
Published: September 2014

Abstract
Renewal theory is one of the most useful tools used in reliability modeling and analysis. The stochastic structure of most models involve an embedded renewal process where the state of the system regenerates itself over these renewal times. This allows one to analyze various issues related to reliability, availability, maintenance, and repair using renewal theory. In this setting, we aim to explore this idea further by considering Markov renewal models. However, we propose to use a Markov renewal process to model a mission‐based system where the device is designed to perform a random sequence of phases or stages of the mission with random durations. The mission process is modeled as a Markov renewal process and different reliability measures are analyzed using Markov renewal theory. First, it is assumed that the system is replaced with a brand‐new one (maximal repair model) at the beginning of each stage. Under this hypothesis, the probability of survival until a predetermined time (system reliability) and the probability of completing a predetermined number of phases of the mission (mission reliability) are determined. Then, we consider cases without replacement (no repair model) and use intrinsic aging concepts to analyze mission reliability of a series system.
Burn‐In and Maintenance Policies
Authors: Mi Jie 
Published: September 2014

Abstract
It is always desired that products have high reliability, long life, and so on. There are basically three phases in which certain measures can be taken to achieve these goals. In the first phase the product has to be carefully designed, good materials have to be used, and so on. This phase will not be considered in this article. In the second phase, i.e. after the completion of production process and before shipment to customers, the burn‐in procedure can be applied to eliminate weak products and thus improve the quality of products before they are shipped. In the third phase, appropriate maintenance measures will keep the products properly functioning for long and accomplish the desired mission with low risk.
Dynamic Programming Methods in Repair and Replacement
Authors: Bhattacharjee Manish C. 
Published: September 2014

Abstract
Applications of dynamic programming to problems in reliability, with a specific emphasis on repair and replacement are surveyed. A brief summary of early classic work on such uses of dynamic programming is followed by a review of later approaches adopted by different researchers toward the problem of modeling plausible and pragmatic notions of imperfect repairs. We then provide a synthesis of how such different approaches for the study of repairable systems can be subsumed within a unified setting of a stochastic dynamic programming framework with some illustrative applications of such a formulation.
Accelerated Life Tests: Designs Comparison within a
B
ayesian Framework
Authors: Dorp Johan René , Mazzuchi Thomas A. , Garcidueñas Jorge E. 
Published: September 2014

Abstract

Accelerated life testing (ALT) is the set of procedures used to reduce the time needed to obtain information related to life characteristics of an item, material or part of interest. The importance of the design of these ALTs is paramount to obtain inferential life estimators with, for example, minimum variance. Typically, for a set ALT, step‐regimen optimal stress levels and duration are determined in the ALT design phase. Herein we focus on the comparison of different ALT designs (fixed stress, profile ALT, progressive step‐stress ALT, and regressive ALT) within a single Bayesian inference framework. We analyze the preposterior variance of the use stress reliability based on a single failure over the course of the ALT for these different ALT designs. To the best of our knowledge Miller and Nelson in their 1983 article titled “Optimum simple step‐stress plans for accelerated life testing” in
IEEE Transactions on Reliability
R–32
, 59–65, were among the earliest to claim similarity of asymptotic estimator variance when comparing step‐stress testing ALT with fixed stress ALT. The results in this article shed some light on this claim within a Bayesian context.
Accelerated Life Tests: Nonparametric Approach
Authors: Ebrahimi Nader 
Published: September 2014

Abstract
Many of today's products are capable of operating under extremes of environmental stress and for thousands of hours without failures. For a variety of reasons, manufacturers may wish to obtain reliability results for their products more quickly than they can with data obtained under normal operating conditions. Consequently, traditional life data analyses are no longer sufficient to identify design weaknesses or validate life prediction.
Accelerated life testing (ALT) is an approach for obtaining more information from a given test time than would normally be possible. It does this by subjecting items to test environment that is more severe than those experienced during normal use. ALT uses a model relating reliability (a life) measured under high‐stress conditions to that which is expected under normal operation to determine the reliability (or length of life). Various parametric and nonparametric models have been introduced to describe the relationship between the distribution functions of lifetimes for different stress levels. The parametric models assume a particular form of the distribution functions for different stress levels and the relationship between them. The approach in this article is nonparametric, where only a relationship between the distribution functions corresponding to any two different stress levels is assumed.
Density and Failure Rate Estimation
Authors: Müller Hans‐Georg , Wang Jane‐Ling 
Published: September 2014

Abstract
Density and failure rate estimation are valuable tools to assess and explore the occurrence and timing of failures in reliability and quality control. This article focuses on nonparametric approaches such as histograms, the kernel method, and other smoothing procedures. Nonparametric density and failure rate estimation is particularly valuable for exploratory data analysis and in situations where available information is insufficient to specify a parametric model, as these methods “let the data speak for themselves”, and no assumptions are needed beyond smoothness of the functions to be estimated. Issues specific for nonparametric approaches are finite bias behavior and choice of the necessary smoothing parameter. For failure rate estimation, available data are sometimes incompletely observed (censored) or available in aggregated form only, necessitating appropriate adjustments. Shape restrictions such as increasing failure rate (IFR) are also occasionally of interest in reliability.
Multiple Sampling Plans
Authors: Suresh Kodakanallur Krishnaswamy , Göb Rainer 
Published: September 2014

Abstract

Under a single sampling plan, the decision of rejection or acceptance is based on a unique sample. If product quality is extreme, either very good or very bad, the inspection amount can be reduced by
iterative sampling plans
: smaller sample portions are taken and evaluated successively so that extreme product quality is detected rapidly. Two kinds of iterative sampling procedures are distinguished: Under
multiple m‐stage sampling
a maximum number of
m
sample portions are selected, a definite decision is reached after evaluating the
m
th sample.
Sequential sampling plans
impose no upper limit on the number of samples taken. The procedure may continue until 100% inspection of a lot. The article describes the operation of iterative sampling plans, provides their essential performance measures, namely the operating characteristic function and the average cumulative sample size, and compares iterative against single sampling.
Skip Lot and Chain Sampling
Authors: Balamurali Saminathan , Jun Chi‐Hyuck , von Collani Elart 
Published: September 2014

Abstract
This article deals with sampling and inspection policies, which are located in the frontier area between process control and lot acceptance sampling. These methods show that from the beginning of statistical quality control the need was felt to consider lot acceptance not as an isolated problem, but in connection with the corresponding lot generating process. However, the methods described here are heuristic ones and do not allow reliable statements about their performance under realistic conditions. Therefore, future research in lot acceptance should focus on modeling the relation between lot quality and process quality for allowing the development of reliable and efficient procedures.
Prior Information in Sampling Schemes
Authors: Seidel Wilfried 
Published: September 2014

AbstractThe choice of a sampling scheme and the amount of inspection is guided at least informally by prior information on quality. We are concerned with formal models of prior information. The Bayesian approach is introduced in detail. Based on a prior distribution of submitted lots according to quality and a cost model, a Bayesian sampling plan minimizes the average costs. A special feature of sampling inspection is that the prior distribution (and consequently also posterior distributions and costs) may have a frequentist interpretation (in contrast to a subjective prior distribution). The cost model is not restricted to costs in the monetary sense. It only requires a numerical goal function that measures the consequences of possible decisions, examples are given.Bayesian models may be used as a theoretical instrument for analyzing and comparing the performance of existing sampling schemes. In practice, assumptions like the stability of the prior may not be satisfied and full information on the prior distribution and the costs is often lacking. Modifications of the full Bayesian approach are necessary, including robust strategies and Γ‐minimax. Estimation of the prior distribution from past inspections and empirical Bayes is discussed. Adaptive sampling strategies are addressed.
Variables Sampling under Measurement Error
Authors: Wilrich Peter‐Th. 
Published: September 2014

Abstract
Single sampling plans for inspection by variables in the case of measurement error are presented that fulfill given requirements on their operating characteristic function. Three cases are considered: (a) measurement standard deviation and process standard deviation known, (b) measurement standard deviation and process standard deviation unknown and each sampled item measured once, and (c) measurement standard deviation and process standard deviation unknown and each sampled item measured more than once.
Acceptance Sampling in Modern Industrial Environments
Authors: Von Collani Elart , Göb Rainer 
Published: September 2014

Abstract
In the industrial mass production environments of the first half of the twentieth century, statistical product inspection (SPI) was a central tool of statistical quality control (SQC). Contemporary SQC emphasizes methods of provision and prevention such as experimental design or process control rather than inspection. To remain a valid tool of modern SQC, SPI has to be redefined in its purpose and in its procedures. This article analyzes important challenges for SPI in modern industrial environments: the adjunct role of SPI in product control, adaptiveness and flexibility of SPI, the role of prior information on product quality, the economic concern of SPI, and quality measurement as a purpose of SPI.
Sampling in Pharmaceutical and Chemical Industries
Authors: Melgaard Henrik 
Published: September 2014

Abstract
This article discusses the specific issues related to sampling in the pharmaceutical and chemical industry. The effect of measurement uncertainty in case of acceptance sampling by variables is one issue. The largest deterioration on the operating characteristics is seen from the low‐frequency error type. Sampling in bulk materials and retesting is also discussed. The trend in this industry is in the direction of faster measurement methods replacing off‐line by on‐line measuring and release.
Survey Quality and Survey Ethics
Authors: Djerf Kari 
Published: September 2014

AbstractProducers of statistics have devoted considerable efforts to improve the quality of their products. Traditionally, surveys are annotated with quality descriptions providing the clients with support to interpret the results. Furthermore, there can be studies on the quality of the entire sample survey, calledquality reports. The clients often need more profound information on the organization, applied procedures and processes as well as the products. Thus, the approach is changing from a mere focus on product quality to total quality management (TQM).This article describes various perspectives of survey quality, starting from traditional reporting and ending up with recent more comprehensive quality approaches.
Repairable Systems: Statistical Inference
Authors: N. Agustin Ma. Zenia 
Published: September 2014

AbstractThis article discusses statistical inference procedures for a general class of models for repairable systems. In particular, estimation procedures as well as goodness‐of‐fit testing for an age‐dependent minimal repair model that incorporates covariates are presented. Examples involving real datasets are used to illustrate the applicability of the procedures.