bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inspection Policies for Reliability
Authors: Baker Rose D. 
Published: September 2014

Abstract
The rôle of inspections in improving reliability is discussed, and a distinction is drawn between inspections of standby equipment, that “waits to work”, and inspections of equipment such as a production line that is in continuous use. Simple examples of both situations are given, and some of the complexities of the various models used are discussed and referenced. It is stressed that models are valueless for practical purposes without checking that they are appropriate, and without estimation of their parameters.
Imperfect Repair
Authors: Hollander Myles , Samaniego Francisco J. , Sethuraman Jayaram 
Published: September 2014

Abstract

The notion of “imperfect repair”, as introduced by Brown and Proschan, is reviewed, and generalizations by Block, Borges and Savits and by Kijima are discussed. Basic properties of these models are highlighted, including the proportional hazards property of the time until first perfect repair and a number of preservation properties enjoyed by these models. The authors summarize the published work on statistical inference based on data assumed to follow an imperfect repair model (i.e., given failure times plus mode of repair indicators), including constrained and unconstrained nonparametric maximum likelihood estimation and estimation
via
nonparametric Bayes methods.
Load‐Sharing Models
Authors: Kvam Paul H. , Lu Jye‐Chyi 
Published: September 2014

Abstract
Load sharing describes how the failure probabilities of components within a system depend on the working status of the other components. For example, if a component fails in the system, the other components may experience an increase in failure rate by inheriting an extra “load” from the failed one. Load sharing has its roots in stress‐strength models for fiber bundles, but extends to many time‐to‐failure test environments, including software reliability and materials testing. Load‐share rules characterize how the load changes after a component within the system fails, and can be estimated if unknown.
Maintenance Optimization
Authors: Mazzuchi Thomas A. , Noortwijk Jan M. , Kallen Maarten‐Jan 
Published: September 2014

Abstract
Maintenance optimization is the problem of determining cost‐optimal maintenance decisions for a system or structure to ensure a safe and economic operation. An important concept in maintenance optimization is that of life‐cycle costing, where the total costs of design, building, maintenance, and demolition are considered over the entire life span of the system or structure in question. In optimizing maintenance, the uncertainties in the time to failure and/or the deteriorating condition should be taken into account. Proper stochastic deterioration models are Markov processes with independent increments (e.g., gamma processes) and Markov decision processes. This chapter reviews mathematical decision models to optimize time‐based maintenance (e.g., in terms of age‐ and block‐replacement intervals) and condition‐based maintenance (e.g., in terms of inspection intervals). Using renewal theory, optimal maintenance decisions under uncertain deterioration can be determined for which the expected nondiscounted cost per unit time or the expected discounted cost over an unbounded time horizon is minimal. Using Bayesian statistics, these optimal maintenance decisions can be adapted on the basis of observations.
Multivariate Imperfect Repair Models
Authors: Ebrahimi Nader 
Published: September 2014

AbstractBrown and Proschan introduced univariate imperfect repair models, in which a system, upon failure, is replaced with a new one with probability α (perfect repair), and is minimally repaired with probability 1 − α (imperfect repair). This article deals with multivariate versions of imperfect repair models for systems ofkcomponents with dependent lifetimes.
General Minimal Repair Models
Authors: Aven Terje 
Published: September 2014

Abstract
Minimal repairs have been given considerable attention in the reliability literature. A minimal repair restores the system to the state it had just before failure. But the state just before failure depends on the information that is available about the system. This article presents general models for analyzing such repairs, using a point process framework and taking into account the information available about the state of the system. Several examples demonstrate the wide range of applications.
Multistate Reliability Theory
Authors: Natvig Bent 
Published: September 2014

Abstract

An inherent weakness of traditional reliability theory (
see
Coherent Systems
) is that the system and the components are always described either as functioning or failed. Early attempts to replace this by a theory of multistate systems for multistate components were made in the late 1970s. By the mid‐1980s the basic theory in this area was established. The objective of this article is to introduce concepts of multistate systems and present some upper and lower bounds for the availabilities and unavailabilities, to any level, in a fixed time interval. A series of applications of multistate reliability theory have been suggested during the last years. For example, the theory enables one to consider applications in electrical power generation systems, where the system state is the amount of power generated by the system or in offshore gas pipeline networks, where the system state is the amount of gas delivered.
Masked Failure Data: Competing Risks
Authors: Basu Sanjib 
Published: September 2014

AbstractThe competing‐risks model considers a setting when a system is exposed to multiple risks and a system failure can be caused by one of the multiple risks. The case of masked causes arises when the causes of failure for some of the systems are not completely identified but are narrowed down to a subset of the potential risks. This article provides a selective review of statistical models and methods for analysis of competing‐risks data with or without masking in the context of reliability applications. Latent failure times and cause‐specific hazards provide two parallel approaches to competing‐risks models; identifiability issues in these models in both nonparametric and parametric settings are discussed. For the case of masked data, recent developments in both likelihood‐based and Bayesian approaches are reviewed. A special emphasis is given to the treatment and models for the masking probabilities. The article concludes with a review of different reliability applications.
Age‐Dependent Minimal Repair and Maintenance
Authors: Sheu Shey‐Huei , Chien Yu‐Hung 
Published: September 2014

Abstract

This article presents and discusses two generalized maintenance replacement policies (policy 1 and 2) for a system with two types of failures, when it fails. Type I failure is assumed to be minor and can thus be rectified through minimal repair. Type II failure is catastrophic and can only be removed by replacement. Under policy 1, a system is replaced at the
n
th type I failure or first type II failure or at age
T
, whichever occurs first. Under policy 2, if an operating system fails before time
t
, it is either replaced by a new system (type II failure) or it undergoes minimal repair (type I failure); otherwise, the system is replaced when the first failure after
t
occurs or the total operating time reaches age
T
(0 ≤
t
≤
T
), whichever occurs first. Type I and type II failures are age dependent. The cost of the
i
th minimal repair of a system at age
y
depends on the random part
C
(
y
) and the deterministic part
c

i

(
y
). The expected cost rates for operating a system under policy 1 and 2 in the long run are obtained, respectively. The optimal (
n
*,
T
*) for policy 1 and optimal (
t
*,
T
*) for policy 2, which minimize the corresponding cost rate are derived and discussed. Various special cases are included and numerical examples are finally demonstrated. Because the models and its analysis are general, several existing results are shown to be subsumed by these two models.
Repair Data, Sets of: How to Graph, Analyze, and Compare
Authors: B. Nelson Wayne 
Published: September 2014

Abstract
Many products accumulate repeated repairs and repair costs over time. Analysis of such recurrence data requires special statistical models and methods not covered in basic reliability books. This article presents a simple and informative model and plot for analyzing data on numbers or costs of repeated repairs of a random sample of units. The plot is illustrated with transmission repair data from preproduction cars on a track test. This article also presents a method for comparing two such datasets, illustrated with automatic and manual transmissions. Computer programs that calculate and make the plots and comparisons with confidence limits are surveyed.
Accelerated Life Tests:Bayesian Design
Authors: Soyer Refik 
Published: September 2014

AbstractIn this article, the Bayesian decision theoretic setup for design of accelerated life tests (ALT) is presented. Some of the key contributions to the Bayesian design of ALT are reviewed. While doing so, approximate Bayesian designs based on linear Bayesian methods and Monte Carlo–based methods are discussed. Computational issues in the evaluation of expectation and optimization steps in the solution of the decision problem are considered and some recent Monte Carlo approaches that can reduce the computational effort in the design problem are discussed.
Accelerated Life Tests: Analysis with Competing Failure Modes
Authors: Bunea Cornel , Mazzuchi Thomas A. 
Published: September 2014

AbstractMost of the accelerated life tests (ALT) ignores the possibility of competing modes of failure. The literature that attempts to address this problem often does so by assuming independence among the competing failure modes. Rather, the failure modes display a highly dependent structure, which is usually influenced by the applied stress. The dependent ALT‐competing risks models available are based on multivariate distributions with specified marginals. However, multivariate exponential and Weibull distributions—dominant in literature—have properties not suited for all applications. This article investigates the applicability of existent independent and dependent models on a couple of datasets, and presents a new ALT model with dependent competing failure modes. The dependence structure is modeled by a copula given the rank correlation. The effects of the dependence degree of the competing failure modes are also studied.
Prediction of Expected Fatigue Lives of Fiber Reinforced Plastic Joints
Authors: Momenkhani Kourosh , Sarkani Shahram 
Published: September 2014

Abstract
This paper scrutinizes the accuracy of the application of the probability distribution functions for predicting the fatigue lives of different fiber reinforced plastic (FRP) joints under variable loading. Three different joint configurations (bonded, bolted, and bonded‐bolted) were tested under constant‐ and variable‐amplitude fatigue loading. The constant‐amplitude fatigue test results were analyzed in both log‐log and semi‐log scales. From there, Rayleigh approximation and Gaussian cumulative functions were used to predict expected fatigue lives. This study shows that even though Rayleigh approximation provides better fatigue lives than Gaussian cumulative function for higher root‐mean‐square (RMS) levels, it falls behind in predicting fatigue lives in lower RMS levels. However, both methods are weak in approximating the experimental fatigue lives.
Software Failure Data Analysis
Authors: Kenett Ron S. 
Published: September 2014

AbstractEffectively and efficiently analyzing software failure data is critical to competitive organizations developing software. This article covers several methods used to analyze such data, with examples. The methods covered include software reliability models for predicting the number of hidden faults, software trouble assessment for assessing the inspection process, Pareto charts of software errors, and control charts for tracking the weekly number of new and fixed bugs.
Life Testing from a
B
ayesian Perspective
Authors: Pulcini Gianpaolo 
Published: September 2014

Abstract

Life testing provides to the Bayesian analyst new information, which may lead to the revision of a belief held prior to observing the experimental data (
learning from experience
). The revised belief, expressed in terms of a posterior probability, constitutes the inferential result. Clearly, in a decision‐making approach, revising belief leads the expert to confirm or change the choice that would have been made among alternative courses of action on the basis of prior belief only. The way in which the observed data modify the current belief is formalized by Bayes theorem. As newer data is observed, today's posterior information becomes tomorrow's prior information, so that each time new observed data come in the current belief can be revised.

Clearly, the effect of life testing in updating expert belief depends on the size of the observed sample and on the manner in which the data have been collected, i.e. on the
design of life testing
. In a Bayesian perspective the choice of life testing design is made by optimizing the design with respect to a given overall loss function, which is the sum of a
decision loss
, associated with the need of making a decision under uncertainty, and a
sampling cost
, representing the cost of collecting life testing data.

Simple examples illustrate the Bayesian learning process and design optimization.
Resampling for Lifetime Distributions
Authors: Belyaev Yuri K. 
Published: September 2014

Abstract

In many applications, data sets contain lifetimes of occurred events, e.g., times of failures, diagnoses, censorings, and so on. If we want to use an estimator of a lifetime characteristic of interest, then it is necessary to evaluate the accuracy of this estimator. Resampling methods can be efficiently used to evaluate the accuracy of many estimators if the size of the data with lifetimes is sufficiently large. They are feasible for realization on personal computers. Correctly applied resampling methods give asymptotically consistent evaluation of the accuracy of estimators under certain assumptions as the size
n
of the data unboundedly grows. The basic tools of mathematical statistics, the likelihood and the central limit resampling theorem, are used to justify resampling methods.
Polya Trees and Their Use in Reliability and Survival Analysis
Authors: Hanson Timothy E. 
Published: September 2014

AbstractThe nonparametric Polya tree prior generalizes traditional parametric models such as the Weibull family. Relative to other well‐known priors, such as Dirichlet, gamma, and beta processes, Polya trees have seen more limited use. However, the Polya tree prior has several attractive features, including the existence of survival densities and the ability to be incorporated in different semiparametric models.
Sampling Inspection of Products
Authors: Göb Rainer 
Published: September 2014

AbstractProduct assessment by inspection is a traditional tool of industrial quality control. Statistical product inspection (SPI), also calledacceptance sampling, is product assessment by sampling inspection. Appropriately conceived, SPI continues to be a valid tool of quality control in modern industrial environments. The article analyzes the basics of SPI in several aspects: history, position of SPI in modern quality control, quality modeling, sampling and its statistical analysis, design of SPI schemes.
Sampling in Industrial Standards
Authors: Palcat Frank 
Published: September 2014

Abstract
This article reviews the history leading up to the development of the first sampling‐related standards from research in the 1920s following through to the very formative years of World War II and beyond. The transition in subsequent years from military‐specific standards to national standards and then to international standards is also covered. Finally, the array of sampling inspection standards currently available from the International Organization for Standardization (ISO) is briefly reviewed.
Rectification Sampling Schemes
Authors: Göb Rainer 
Published: September 2014

Abstract

Statistical sampling plans lead to a decision between accepting or rejecting a lot. Rejection calls for some extraordinary intervention into the routine business flow. A specific type of intervention consists in a more extensive inspection of the lot with the aim of
rectification
, i.e. removing or replacing detected items of inappropriate quality. Sampling schemes with a rectification interpretation of rejection are called
rectification schemes
. The article introduces the procedures and performance measures of rectification schemes. The well‐known
Dodge–Romig rectification scheme
is analyzed in detail.
Reliability Sampling
Authors: Hryniewicz Olgierd 
Published: September 2014

Abstract

Reliability sampling is a part of acceptance sampling dedicated to the control of those quality characteristics of a product, which are related to its functioning in time. Therefore, in reliability sampling we are interested in such quality characteristics of a product as: mean time to failure (MTTF), mean time between failures (MTBF), probability of good performance during a specified mission time, and so on. The aim of the reliability sampling procedures, known as
sampling plans
, is to provide means for testing requirements related to these characteristics. In the article we present reliability sampling plans by attributes and variables, which are useful in reliability practice. For sampling plans by variables, we present procedures valid in cases of the exponential and the Weibull distributions of lifetimes. We consider different types of sampling plans with different types of censoring. Special attention is devoted to procedures described in international standards related to reliability.
Attributes Sampling under Classification Error
Authors: Greenberg Betsy S. 
Published: September 2014

Abstract
Inspection, particularly acceptance sampling, is an important aspect of quality control. An acceptance sampling plan is one in which a specified number of units is sampled from each lot, with the lot being accepted if fewer than a fixed number of nonconforming items are found in the sample. However, inspection errors are inevitable. Inspection procedures may commit two types of errors. Items that are classified as nonconforming may be conforming (false positive) and conforming items may be classified as nonconforming (false negative). These errors affect the number of nonconforming items that are identified as well as the outgoing quality obtained from acceptance sampling plans.
Database Systems for Acceptance Sampling
Authors: Lenz Hans‐J. 
Published: September 2014

Abstract

Data collected in on‐line or off‐line quality control are no longer read into files owned by single departments but are stored in a database system. The data can be accessed according to the user's view (profile) and his read/write rights. The use of such operative data is called
on‐line transaction processing
(OLTP). It is of interest to low ranked officers responsible for administrative tasks and concerned with workflows like incoming batch inspection. Middle managers need data for planning and decisions, i.e., for “on‐line analytical processing” (OLAP), where grouped and aggregated data are mandatory. Such databases are called
data warehouses
and the managers' views are known as
data marts
. The main design ideas of such systems are presented.
Sampling from Virtual Populations
Authors: Polansky Alan M. 
Published: September 2014

Abstract
The increasing complexity of the problems addressed in modern statistical inference has created many situations where a closed‐form analytical solution is not available. For example, when constructing a rejection region for a hypothesis‐testing procedure, the sampling distribution of the test statistic under the null hypothesis may not have a closed form, even when the exact form of the population is known. In nonparametric situations where the population may not be known, estimates of the sampling distribution of a test statistic or parameter estimate computed using the bootstrap often do not have a closed form. The use of empirical approximations based on simulated realizations of the random process that produces the observed data have been implemented for many years. Almost universal access to computers has made such simulations easy to perform. The purpose of this article is to review the most common methodologies for performing these simulations.
Adaptive and Spatial Sampling Designs
Authors: Siegfried Gabler 
Published: September 2014

Abstract
On the one hand, spatial and adaptive sampling designs are informative since they depend on the characteristic of interest—in contrast to the classical sampling designs such as simple random sampling. On the other hand, better results can be obtained using adaptive designs for populations with special patterns. Dealing with spatial inferences, model‐based approaches are often used. The fixed‐population approach is replaced by a stochastic one. The characteristic of interest is defined for a continuous region of space and time that can be described by a spatial stochastic process.
Selection and Validation of Response Scales
Authors: Ograjenšek Irena 
Published: September 2014

Abstract
Measurement is defined as a set of rules for assigning either numbers or numerals to empirical properties of a concept under consideration. Concepts can be operationalized as variables with different properties of measurement scale: nominal, ordinal, interval, and ratio. The level of measurement determines which mathematical operations are possible on a variable, which, in turn, define the tools of statistical analysis at researchers' disposal.
Specific measurement tools applicable in the survey setting include list questions, category questions, ranking questions, and rating or scale questions. We want these measures to be reliable and valid, capturing what they are supposed to capture.
In the process of measurement, data can be obtained either in formal or informal settings and can represent verbal (oral and written) as well as nonverbal acts and/or responses. In order to minimize the possible effect of the chosen data collection method on the validity and/or scope of the research findings, researchers can use two or more methods of data collection to test hypotheses and measure variables; this is the essence of the so‐called triangulation.
Processing of Survey Data
Authors: Biemer Paul P. 
Published: September 2014

AbstractUpon completion of data collection, the survey data must be converted from its raw and unedited state to a verified and corrected state that is ready for analysis and/or dissemination to the users. These steps may include questionnaire inspection, data entry, editing, coding, data preparation, documentation, and data analysis. This chapter discusses these steps as well as their potential effect on data quality.