bluuebunny/crossref_metadata_2025_split huggingface dataset:
Condition Monitoring
Authors: Banjevic Dragan , Jardine Andrew K.S. 
Published: September 2014

Abstract

Condition monitoring (CM) is a set of various techniques and procedures used in industry to measure the “parameters” of the state/health of equipment, or to observe conditions under which the equipment is operating. People apply CM for early detection of signs of malfunctioning and faults, and then for fault diagnosis and timely corrective or predictive maintenance. The whole combination of CM data acquisition, processing, interpretation, fault detection, and maintenance strategy is called the
CM system
/
program
(alternatively, condition‐based maintenance (CBM)). The most common CM techniques are vibration analysis, tribology (oil/debris analysis), visual inspections, current monitoring, conductivity testing, performance (process parameters) monitoring, thermal monitoring, corrosion monitoring, and acoustic (sound/noise) monitoring. The three major steps in a CM system are data acquisition, data processing, and data assessment for decisions (maintenance decision making and fault diagnostics and prediction). This article describes the key points of all three major steps, including CBM; gives a short history of CM; discusses the implementation, advantages and disadvantages of CM; comments on the future development of CM; and recommends further reading. An example of CM implementation is also included.
Maintenance Modeling and Management
Authors: Scarf Philip A. 
Published: September 2014

AbstractThe specification of a maintenance design is at the heart of maintenance modeling and management. This design is essentially a structured description of the system of interest in terms of its parts (components, sockets, and their interrelationships), and prescribed maintenance rules for the components in the system. The maintenance rules are determined by the following: the risk that component failure poses to system performance, the economic priorities of the system owner and the maintainer, the availability of data relating to components, and the interrelationships beteen components within the system. This article describes how one might determine a structured description of a system of interest, how maintenance actions for the resultant components might be chosen, and then how these maintenance actions may be optimized using maintenance models to provide the system owner with a set of efficient and effective maintenance rules for the system.
Reliability Optimization (Multistate and Multiobjective)
Authors: Coit David W. 
Published: September 2014

Abstract
Reliability optimization involves the simultaneous evaluation and selection of components and a system‐level design configuration that collectively meets all design constraints, and at the same time, optimizes some objective function, often system reliability or availability. Often these problems are very constrained because of physical or budgetary limitations. There are several variations of reliability optimization and there have been many attempts to determine optimal design configurations for different problem formulations. Applicable optimization methods include dynamic programming, integer programming, mixed integer and nonlinear programming, and metaheuristics, e.g., genetic algorithms (GA). Notable extensions to this problem include multistate reliability optimization and multiple‐objective system reliability optimization.
Quantitative Reliability Assessment of Electricity Supply
Authors: Billinton Roy 
Published: September 2014

Abstract
This paper illustrates the hierarchical relationship of the basic functional zones of generation, transmission, and distribution in an electric power system. The techniques used to quantitatively assess the reliability of the overall system and the hierarchical levels are briefly discussed. The importance in reliability assessment of consistent and comprehensive data on relevant equipment, load characteristics, and system constraints is illustrated.
NoFaultFound
Authors: Jones Jeffrey A. 
Published: September 2014

AbstractThis article describes the No Fault Found (NFF) phenomenon that occurs when a system is tested after a failure is reported but no failure is found during the test. This basic phenomenon is known by a large number of terms, which are often abbreviated to three letter acronyms, and are described in this article. The level of occurrence of such false positive faults in various technical fields is described and a number of possible reasons for the incidence are discussed. Finally a discussion of the management of such faults within the context of quantitative risk assessment is included.
Probabilistic Risk Assessment
Authors: Modarres Mohammad 
Published: September 2014

AbstractThis article summarizes the formal approach to probabilistic risk assessment (PRA) of complex engineered systems. It is important to stress that the main objective of performing a PRA is not necessarily the estimation of the actual risk values; rather it is the determination of those elements of the system that substantially contribute to the estimated risks, characterization of uncertainties associated with such estimates, and assessment of the effectiveness of the various risk reduction strategies available. Performing PRA for a complex engineered system is a considerably intense and time‐consuming process that requires coordinated cooperation of different individuals and subject matter experts from research, engineering, and operations all the way to decision‐making and regulatory authorities. The main goal of this article, however, is to provide the readers with a high‐level, application‐independent overview of the major elements of a formal PRA. In the first section, critical concepts have been defined and the PRA process has been outlined. In the remainder of the article, a step‐by‐step procedure for conducting a PRA has been discussed, including limitations of each step. In each section, appropriate references for further details about the PRA process have been discussed.
Design of Reliability Tests
Authors: Kececioglu Dimitri B. , Chen Xiaofang 
Published: September 2014

Abstract
Reliability is the probability that a product can perform its required functions for a specified function period without failure under certain environments and stress levels. Reliability information is very important to manufacturers. It enables them to produce better products for their costumers. Reliability tests are necessary in designing and manufacturing a product to guarantee its reliability and quality. This article introduces the basic ideas of how to design reliability tests, their objectives, test types, and test procedures.
Burn‐in Testing: Its Quantification and Applications
Authors: Kececioglu Dimitri B. , Chen Xiaofang 
Published: September 2014

Abstract
Early life failures are usually attributed to problems in the manufacturing, packaging, and assembling phases. After making improvements to the greatest possible extent in these phases, the only way to improve the product's reliability is to eliminate defective units before they reach customers. A common approach used to weed out defective and weak parts before shipping the products to customers is burn‐in testing. Burn‐in testing has become a frequently used procedure in many industries, especially the semiconductor industry. In this article, an overview of burn‐in testing is presented and its applications are explained. Burn‐in methods are related to a variety of failure mechanisms and the conditions under which the tests are run are specified. Finally, the burn‐in time is determined through a reliability bathtub curve model to be the time after which the failure rate remains constant.
Accelerated Life Testing
Authors: Donahoe Daniel , Zhao Ke , Murray Steven , Ray Rose M. 
Published: September 2014

Abstract
Accelerated testing is a set of methods that attempts to replicate and predict the quantitative effect on product life of what are assumed to be the most important degradation mechanisms determining product life in actual service. In all accelerated testing approaches, one or more physical degradation methods are applied to promote failure. The history and limitations of accelerated testing and mathematical tools for modeling product lifetime as a function of acceleration mechanisms are discussed.
Systems Reliability
Authors: Andrews John D. 
Published: September 2014

Abstract
It is necessary to make predictions of the failure frequency or likelihood of systems operating in many industrial sectors. The techniques available to conduct this type of assessment require the component failure and repair characteristics along with information defining the system structure. This article describes the features of some of the techniques that can be used to conduct such system reliability assessments. For each method discussed, an overview of its capability is provided. Consideration is given to the use of systems reliability assessment techniques in the context of risk assessment. Finally, a discussion addresses when each of the methods described should be used.
Reliability of Large Systems
Authors: Kołowrocki Krzysztof 
Published: September 2014

AbstractThis article is concerned with the application of limit reliability functions to the reliability evaluation of large systems. Two‐state large nonrepaired systems composed of independent components are considered. The asymptotic approach to the system reliability investigation and the system limit reliability function are defined. Two‐state homogeneous series and parallel systems are defined and their exact reliability functions are determined. The classes of limit reliability functions of these systems are presented. This article contains an exemplary application of the presented facts to the reliability evaluation of large technical systems. The accuracy of this evaluation is illustrated. Brief review and the latest references on limit reliability functions of two‐state and multistate homogeneous and nonhomogeneous series, parallel, and other systems are presented as well.
Repairable Systems Reliability
Authors: Rigdon Steven E. 
Published: September 2014

Abstract
Nonrepairable systems fail just once, so models for nonrepairable systems must account for random lifetimes. It is often reasonable to assume that different units have random lifetimes that are independent and follow the same distribution, leading to the usual i.i.d. (independent and identically distributed) assumption. By contrast, models for repairable systems must account for successive failures in time. For a given system, these times between failure are often not independent and not identically distributed. Various assumptions about the failure process lead to different models for repairable systems. In this article, we present the nonhomogeneous Poisson process (NHPP), the renewal process, the piecewise exponential model, and the modulated power law process (a compromise between renewal process and NHPP). Inference for a single system as well as for multiple copies of a system is discussed. We also discuss briefly the use of covariates or concomitant variables.
Reliability of Consumer Goods with “Fast Turn Around”
Authors: Ouden Elke Den , Lu Yuan , Brombacher Aarnout 
Published: September 2014

Abstract
Development of consumer goods that meet the buying and using expectations of the consumers is no longer a straightforward job. Technology becomes available at increasing pace, while at the same time the product life cycles ever shortening. Companies are challenged to bring innovative products faster to the market, with inherently higher uncertainties on the consumer expectations of these products. In this context, reliability has a different meaning and the design methods and tools required to manage reliability, as well as test strategies in this context, are different from the classical ones.
Non‐ and Semiparametric Models and Inference for Reliability Systems
Authors: Peña Edsel A. , Taylor Laura L. 
Published: September 2014

AbstractPrecise and reliable knowledge of the performance of deployed systems in the engineering, biomedical, public health, military, economic, and business areas enables an informed assessment of the risk of failure and/or the quality of systems. In the study of the risk and the quality of such systems, a conceptual model is that of reliability systems. This article discusses non and semiparametric modeling and inference of reliability systems. In the first section, a review of some basic notions of reliability systems is presented. The next two sections provide nonparametric models when there are no covariates impacting the performance of the system, and the methods of estimating the survivor function for the system life based on possibly right‐censored data. An illustrative example is given for a randomly generated dataset. The next section then discusses semiparametric models, which incorporates covariates, with primary focus on the Cox's proportional hazards model. Inference procedures for these semiparametric models are discussed. Finally, mention is made of dynamic models which are those that incorporate the changes occurring dynamically in a system, as time progresses.
Degradation and Shock Models
Authors: Finkelstein Maxim 
Published: September 2014

AbstractThe standard assumption in shock models are that the failure of a system is caused either by the cumulative effect of a large number of shocks or by a shock that exceeds a certain critical level. In this paper, we discuss both possibilities and consider some generalizations of existing models. Two generic point processes of shocks are considered: the renewal and the Poisson processes. When the average number of renewals to reach the degradation threshold of accumulated damage is large, the asymptotic renewal theory is used for obtaining the first passage probabilities. For the specific case of the homogeneous Poisson process of shocks, some new models with different criterions of failure are considered. For instance, we consider the setting when the failure occurs only when the successive shocks are “closer” than the predetermined value (deterministic or random).
Lifetime Models and Risk Assessment
Authors: Lee Mei‐Ling Ting 
Published: September 2014

AbstractIn this article we review several methods used in analyzing lifetime data. Lifetime data have found many applications in biomedical research, engineering, as well as in social sciences. Lifetime data can be defined as the times to the occurrence of a certain event. Statistical methods for analyzing lifetime data include the definition of survival time, censored data, prediction of probability of survival, prediction of instantaneous hazards, and evaluation of cure rates. We review different types of lifetime data, methods for estimating hazard and survival functions, methods for risk assessment, as well as multivariate methods for survival data. Lifetime models based on stochastic processes hitting a boundary, such as threshold regression for risk assessment, are also discussed.
Managing Infrastructure Reliability, Safety, and Security
Authors: Zimmerman Rae 
Published: September 2014

Abstract
Infrastructure reliability, in its most general sense, refers to whether a system performs as expected. Reliability is relative, depending on the perspective of the infrastructure user. Safety and security are key components for the concept of reliability, since they pertain to basic survival, and are prerequisites for others. Safety and security are defined, and their emergence as core concepts in infrastructure reliability is described. Infrastructure security now relates to a set of critical infrastructures. Factors that potentially compromise infrastructure security include how infrastructure is spatially distributed and interrelated or interdependent with other infrastructures. Risk‐based analytical approaches, and alternatives to these approaches, have arisen to address the likelihood and form of attacks as well as the consequences of attacks on infrastructure. Selected examples of these methodologies are presented. Resource allocation at the federal level emphasizes a risk‐based approach, though the nature of risks faced by critical infrastructures, how these risks should be quantified, what analytical tools should be used to produce such estimates, at what geographic scale, and how they are to be incorporated into prioritization schemes are still a subject of considerable debate.
Product Risk Management: Testing and Warranties
Authors: Chan Wai 
Published: September 2014

Abstract
Manufacturers employ various testing strategies to identify and rectify defects before products are released to the markets. A carefully planned test strategy can improve product quality and customer satisfaction and reduce life cycle cost. Testing can also help reveal potential hazards associated with a product, evaluate associated risks, and discover factors that can be controlled to reduce risks. This article presents some of the tests that are performed during the prototype and production phases. These include the design verification test (DVT) and the highly accelerated life test (HALT) during the prototype phase; and the reliability qualification test (RQT) and the ongoing reliability test (ORT) in the production phase. We also describe how the results of a warranty performance analysis can be used as feedback to improve the test coverage for the tests run during production.
Axiomatic Models of Perceived Risk
Authors: Jia Jianmin , Dyer James S. , Butler John C. 
Published: September 2014

AbstractThis article presents two classes of measures for perceived risk based on decomposition of a lottery into its mean and its dispersion about the mean, or standard risk. One of the classes of risk measures presumes that there is no risk when there is no uncertainty involved, and the other allows different degenerate lotteries to be evaluated with different values of “risk.” The former has more prescriptive appeal in risky decision making, but the latter may have more descriptive power for subjective risk judgments. These risk measures can also incorporate the asymmetric effects of losses and gains on perceived risk when an appropriate form of the standard measure of risk is applied. These perceived‐risk models unify a large body of empirical evidence regarding risk judgments, and provide sufficient flexibility to capture better perceptions of risk than previously developed risk models. Further, they provide clear ways to accommodate financial measures of risk and psychological measures of risk, and they can be incorporated into preference models based on mean‐risk trade‐offs.
Axiomatic Measures of Risk and Risk‐Value Models
Authors: Jia Jianmin , Dyer James S. , Butler John C. 
Published: September 2014

AbstractThis article provides a review of risk and risk‐value models that lead to decision making by explicitly trading off between risk and value. We begin with a discussion of a preference‐dependent measure of risk that is compatible with traditional expected utility theory, and therefore lends itself to an examination of how individuals perceive risky gambles as well as how they choose among them. Further, this risk measure can be the basis for generalizing models of decision making based on the intuitively appealing notion of trading off between risk and value. Based on the two‐attribute expected utility axioms, this risk‐value framework has much more flexibility in modeling preferences than the traditional single‐attribute expected utility theory. Specifically, we describe three useful classes of decision models: moments risk‐value models, exponential risk‐value models, and generalized disappointment models. These decision models unify two streams of research: one in developing preference models and the other in modeling risk judgments. They can also provide prescriptive guidance for those people who are willing to deviate from the traditional expected utility preference models and make their decisions based on risk‐value trade‐offs, as in financial decision making.
Understanding Large‐Scale Structure in Massive Data Sets
Authors: Braverman Amy J. 
Published: September 2014

AbstractThis article discusses massive data sets and some statistical methods for analyzing them. This is not a comprehensive review of all aspects of massive data set analysis, but rather a review and discussion of the issues in the context of statistics. We start with what a “massive” data set is, and then review several analysis methods that have been proposed in the statistics literature. These have certain common threads, most notably their relationships to the concept of data reduction. Then we examine how they can be applied with the specific aim of understanding local structure on a global scale, thus exploiting one important characteristic of massive data sets. We conclude with a brief discussion of the challenges and potential benefits for risk analysis, focusing on an example from climate studies.
Repeated Measures Analyses
Authors: Liu Mengling , Shao Yongzhao 
Published: September 2014

AbstractRepeated measures data arise when multiple measurements of response variables and/or covariates are obtained in sequence from each experimental unit and occur frequently in many disciplines. The repeated measures design provides opportunities for investigators to examine the pattern of response variable under different measurement occasions, to understand the relationship between repeated outcomes and covariates, and to discern the dependence pattern among the repeated measurements. A special feature of repeated measures data is that measurements from the same unit are usually positively correlated; thus, valid and effective analysis of repeated measures must deal with this within‐subject correlation. In this article, we review some of the analysis methods for repeated measures, which include univariate summary methods, multivariate analysis of variance, parametric random‐effects methods, marginal generalized estimation equation methods, and modern semiparametric and nonparametric approaches. Some simple methods for data exploration and graphical display are demonstrated using real data examples. We also briefly discuss related computational tools and statistical packages.
Change Point Analysis
Authors: West Ronnie Webster 
Published: September 2014

Abstract

This entry provides a brief introduction to the statistical methodology called
change point analysis
, which is oft used in risk assessment. The goal of change point analysis is to model changes in the characteristics of a response variable over a range of a predictor variable. The discussion below is framed by two simple examples from the risk assessment literature where the response characteristic of interest is the mean response. The examples illustrate two simple change point methods for detecting the location of the change point in terms of the predictor variable and the nature of the change in the mean response before and after the change point. More complicated change point methods are also discussed briefly.
Intent‐to‐Treat Principle
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana 
Published: September 2014

Abstract
This article provides an overview of the concepts involved in intent‐to‐treat analyses of randomized controlled clinical trials. Implications for the interpretation of the results of randomized trials that arise when noncompliance, missing data, and losses to follow‐up occur are described. Alternative approaches to analysis are described.