bluuebunny/crossref_metadata_2025_split huggingface dataset:
Masked Failure Data:Bayesian Modeling
Authors: Kuo Lynn 
Published: February 2008

AbstractIn the analysis for masked system lifetime data, the exact component that causes the system's failure is often unknown. For each series system at test, we observe its system's failure time and a set of components that includes the component actually causing the system to fail. The objective is to make inferences for the reliability of the components and their diagnostic probabilities. Basic Bayesian models are reviewed. Typically, both Expectation and Maximization (EM) and Markov Chain Monte Carlo (MCMC) algorithms are employed for inferences.Both exponential distributions and Weibull distributions for the component lifetimes and some masking probability models are used here to illustrate these techniques.
Life Cycle Costs and Reliability Engineering
Authors: Elsayed Elsayed A. 
Published: February 2008

Abstract
This chapter presents the life cycle cost (LCC) and relates it to product or system reliability. It discusses LCC through the product life phases beginning with the design phase and ending with warranty and field operation phase. It demonstrates how decisions in every phase directly or indirectly affect the LCC over the product life. Determinations of optimal process parameters or decision that minimize the LCC are demonstrated through examples in different phases of the product life cycle.
Warranty Modeling
Authors: Thomas Marlin U. 
Published: February 2008

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: February 2008

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: February 2008

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.
Imperfect Repair
Authors: Hollander Myles , Samaniego Francisco J. , Sethuraman Jayaram 
Published: February 2008

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: February 2008

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. , Van Noortwijk Jan M. , Kallen Maarten‐Jan 
Published: February 2008

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.
Expert Opinion in Reliability
Authors: Sims Benjamin H. , Koehler Wiedlea Andrew , Wilson Gregory D. 
Published: February 2008

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.
System Availability
Authors: Gaver Donald P. 
Published: February 2008

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.
Multivariate Imperfect Repair Models
Authors: Ebrahimi Nader 
Published: February 2008

Abstract

Brown 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 of
k
components with dependent lifetimes.
General Minimal Repair Models
Authors: Aven Terje 
Published: February 2008

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.
Global and Dynamic Information Measures for Reliability
Authors: Ebrahimi Nader , Soofi Ehsan S. 
Published: February 2008

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.
Cumulative Damage Models Based on Gamma Processes
Authors: Park Chanseok , Padgett William J. 
Published: February 2008

Abstract
A statistical model for cumulative damage based on gamma processes, incorporating an accelerating variable, is derived. A simpler approximation to the resulting failure distribution is also given which is a Birnbaum–Saunders‐type model. The proposed model is illustrated with a real‐data example which shows that it is a strong competitor to the widely used Weibull accelerated test model.
Multistate Reliability Theory
Authors: Natvig Bent 
Published: February 2008

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: February 2008

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.
Reliability Growth Modeling
Authors: Sen Ananda 
Published: February 2008

Abstract
At the initial developmental phase of any production involving complex systems, a test‐analyze‐and‐fix (TAAF) process is often undertaken to promote the detection of system faults and incorporate appropriate redesigns or other corrective actions, and the modified system is retested. As this test–redesign–retest sequence contributes to an improvement in the system performance, failure data become increasingly sparse at the later stages of testing, making it more difficult to assess the current reliability. A reliability growth model provides a structure through which failure data from the current as well as previous stages of testing could be analyzed in an integrated way in order to make efficient inference on reliability, and other process parameters. Reliability growth models have been applied extensively in analyzing failure data arising from hardware as well as a piece of software. Further, for systems with single‐shot missions that undergo a developmental program, reliability growth models arise quite naturally while studying the effectiveness of design fixes. In this article, we describe some popular reliability growth models that have been developed in the context of observing either time to failure or sequence of polytomous outcomes.
Multicomponent Maintenance
Authors: Nicolai Robin P. , Dekker Rommert 
Published: February 2008

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: February 2008

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.
Warranty Cost Analysis
Authors: Murthy D. N. Prabhakar , Blischke Wallace R. 
Published: February 2008

Abstract

For products sold with warranty, the manufacturer incurs costs resulting from the servicing of failures occurring under warranty. These costs depend on several factors, the most important being the reliability of the product. For planning purposes, the manufacturer must estimate (a) the expected cost of warranty per unit sold and (b) the expected cost per unit time over the product life cycle. This must be done in the product development stage. This article deals with models that may be used to obtain these cost estimates for products sold with free‐replacement and
pro rata
warranties.
Age‐Dependent Minimal Repair and Maintenance
Authors: Sheu Shey‐Huei , Chien Yu‐Hung 
Published: February 2008

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.
Warranty Servicing
Authors: Murthy D.N. Prabhakar , Nat Jack 
Published: February 2008

Abstract
Warranty costs are the additional expenses that a manufacturer incurs to service all the claims made by customers. These costs can be reduced by performing effective waranty servicing. In this article, a framework is given to link all the issues involved in warranty servicing and models that a manufacturer can use to make optimal servicing decisions are received.
Sampling Inspection of Products
Authors: Göb Rainer 
Published: February 2008

Abstract

Product assessment by inspection is a traditional tool of industrial quality control. Statistical product inspection (SPI), also called
acceptance 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.
Multiple Sampling Plans
Authors: Suresh Kodakanallur Krishnaswamy , Göb Rainer 
Published: February 2008

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.
Sampling in Industrial Standards
Authors: Palcat Frank 
Published: February 2008

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.
Attributes Sampling Schemes in International Standards
Authors: Balamurali Saminathan , Göb Rainer , Jun Chi‐Hyuck 
Published: February 2008

Abstract
Sampling tables for the inspection of attribute quality characteristics were developed during World War II by the United States Department of Defense. In 1950, these tables led to the first military standard MIL‐STD‐105A which evolved into the latest version MIL‐STD‐105E issued in 1989. Further national and international standards emerged as derivatives of MIL‐STD. On international level, the International Standards Organization (ISO) introduced the attributes sampling standard ISO 2859 which has been adopted as a national standard in many countries. The article analyzes the purpose, structure, and operation of MIL‐STD‐105E and of ISO 2859.
Variables Sampling Schemes in International Standards
Authors: Balamurali Saminathan , Göb Rainer , Jun Chi‐Hyuck 
Published: February 2008

Abstract

Variables sampling schemes are based on the specification range quality model: a specification range
R
is prescribed for a continuous measurement characteristic like weight, length, volume, and an item is considered as conforming, if the associated measurement
X
is in
R
. Issued in 1957, MIL‐STD‐414 was the first industrial standard on variables sampling. On the international level, the International Standards Organization (ISO) introduced in 1989, the first variables sampling standard ISO 3951, compatible with the attributes sampling standard ISO 2859, which is a civilian counterpart of MIL‐STD‐105E. ISO 3951 has been adopted as a national standard in many countries. The article analyzes the purpose, structure, and operation of the latest version of ISO 3951 which was issued in three parts in 2005 and 2006.
Reliability Sampling
Authors: Hryniewicz Olgierd 
Published: February 2008

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.