The Experts below are selected from a list of 138 Experts worldwide ranked by ideXlab platform
J E Strutt - One of the best experts on this subject based on the ideXlab platform.
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probabilistic physics of failure models for Component reliabilities using monte carlo simulation and weibull analysis a parametric study
Reliability Engineering & System Safety, 2003Co-Authors: P L Hall, J E StruttAbstract:Abstract In reliability Engineering, Component failures are generally classified in one of three ways: (1) early life failures; (2) failures having random onset times; and (3) late life or ‘wear out’ failures. When the time-distribution of failures of a population of Components is analysed in terms of a Weibull distribution, these failure types may be associated with shape parameters β having values 1 respectively. Early life failures are frequently attributed to poor design (e.g. poor materials selection) or problems associated with manufacturing or assembly processes. We describe a methodology for the implementation of physics-of-failure models of Component lifetimes in the presence of parameter and model uncertainties. This treats uncertain parameters as random variables described by some appropriate statistical distribution, which may be sampled using Monte Carlo methods. The number of simulations required depends upon the desired accuracy of the predicted lifetime. Provided that the number of sampled variables is relatively small, an accuracy of 1–2% can be obtained using typically 1000 simulations. The resulting collection of times-to-failure are then sorted into ascending order and fitted to a Weibull distribution to obtain a shape factor β and a characteristic life-time η. Examples are given of the results obtained using three different models: (1) the Eyring–Peck (EP) model for corrosion of printed circuit boards; (2) a power-law corrosion growth (PCG) model which represents the progressive deterioration of oil and gas pipelines; and (3) a random shock-loading model of mechanical failure. It is shown that for any specific model the values of the Weibull shape parameters obtained may be strongly dependent on the degree of uncertainty of the underlying input parameters. Both the EP and PCG models can yield a wide range of values of β, from β>1, characteristic of wear-out behaviour, to β The results are discussed in the context of traditional methods for reliability analysis and conventional views on the nature of early-life failures.
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probabilistic physics of failure models for Component reliabilities using monte carlo simulation and weibull analysis a parametric study
Reliability Engineering & System Safety, 2003Co-Authors: P L Hall, J E StruttAbstract:Abstract In reliability Engineering, Component failures are generally classified in one of three ways: (1) early life failures; (2) failures having random onset times; and (3) late life or ‘wear out’ failures. When the time-distribution of failures of a population of Components is analysed in terms of a Weibull distribution, these failure types may be associated with shape parameters β having values 1 respectively. Early life failures are frequently attributed to poor design (e.g. poor materials selection) or problems associated with manufacturing or assembly processes. We describe a methodology for the implementation of physics-of-failure models of Component lifetimes in the presence of parameter and model uncertainties. This treats uncertain parameters as random variables described by some appropriate statistical distribution, which may be sampled using Monte Carlo methods. The number of simulations required depends upon the desired accuracy of the predicted lifetime. Provided that the number of sampled variables is relatively small, an accuracy of 1–2% can be obtained using typically 1000 simulations. The resulting collection of times-to-failure are then sorted into ascending order and fitted to a Weibull distribution to obtain a shape factor β and a characteristic life-time η . Examples are given of the results obtained using three different models: (1) the Eyring–Peck (EP) model for corrosion of printed circuit boards; (2) a power-law corrosion growth (PCG) model which represents the progressive deterioration of oil and gas pipelines; and (3) a random shock-loading model of mechanical failure. It is shown that for any specific model the values of the Weibull shape parameters obtained may be strongly dependent on the degree of uncertainty of the underlying input parameters. Both the EP and PCG models can yield a wide range of values of β , from β >1, characteristic of wear-out behaviour, to β β =∞. In contrast, the shock-loading model is inherently random, and its predictions correspond closely to those of a constant hazard rate model, characterized by a value of β close to 1 for all finite degrees of parameter uncertainty. The results are discussed in the context of traditional methods for reliability analysis and conventional views on the nature of early-life failures.
P L Hall - One of the best experts on this subject based on the ideXlab platform.
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probabilistic physics of failure models for Component reliabilities using monte carlo simulation and weibull analysis a parametric study
Reliability Engineering & System Safety, 2003Co-Authors: P L Hall, J E StruttAbstract:Abstract In reliability Engineering, Component failures are generally classified in one of three ways: (1) early life failures; (2) failures having random onset times; and (3) late life or ‘wear out’ failures. When the time-distribution of failures of a population of Components is analysed in terms of a Weibull distribution, these failure types may be associated with shape parameters β having values 1 respectively. Early life failures are frequently attributed to poor design (e.g. poor materials selection) or problems associated with manufacturing or assembly processes. We describe a methodology for the implementation of physics-of-failure models of Component lifetimes in the presence of parameter and model uncertainties. This treats uncertain parameters as random variables described by some appropriate statistical distribution, which may be sampled using Monte Carlo methods. The number of simulations required depends upon the desired accuracy of the predicted lifetime. Provided that the number of sampled variables is relatively small, an accuracy of 1–2% can be obtained using typically 1000 simulations. The resulting collection of times-to-failure are then sorted into ascending order and fitted to a Weibull distribution to obtain a shape factor β and a characteristic life-time η. Examples are given of the results obtained using three different models: (1) the Eyring–Peck (EP) model for corrosion of printed circuit boards; (2) a power-law corrosion growth (PCG) model which represents the progressive deterioration of oil and gas pipelines; and (3) a random shock-loading model of mechanical failure. It is shown that for any specific model the values of the Weibull shape parameters obtained may be strongly dependent on the degree of uncertainty of the underlying input parameters. Both the EP and PCG models can yield a wide range of values of β, from β>1, characteristic of wear-out behaviour, to β The results are discussed in the context of traditional methods for reliability analysis and conventional views on the nature of early-life failures.
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probabilistic physics of failure models for Component reliabilities using monte carlo simulation and weibull analysis a parametric study
Reliability Engineering & System Safety, 2003Co-Authors: P L Hall, J E StruttAbstract:Abstract In reliability Engineering, Component failures are generally classified in one of three ways: (1) early life failures; (2) failures having random onset times; and (3) late life or ‘wear out’ failures. When the time-distribution of failures of a population of Components is analysed in terms of a Weibull distribution, these failure types may be associated with shape parameters β having values 1 respectively. Early life failures are frequently attributed to poor design (e.g. poor materials selection) or problems associated with manufacturing or assembly processes. We describe a methodology for the implementation of physics-of-failure models of Component lifetimes in the presence of parameter and model uncertainties. This treats uncertain parameters as random variables described by some appropriate statistical distribution, which may be sampled using Monte Carlo methods. The number of simulations required depends upon the desired accuracy of the predicted lifetime. Provided that the number of sampled variables is relatively small, an accuracy of 1–2% can be obtained using typically 1000 simulations. The resulting collection of times-to-failure are then sorted into ascending order and fitted to a Weibull distribution to obtain a shape factor β and a characteristic life-time η . Examples are given of the results obtained using three different models: (1) the Eyring–Peck (EP) model for corrosion of printed circuit boards; (2) a power-law corrosion growth (PCG) model which represents the progressive deterioration of oil and gas pipelines; and (3) a random shock-loading model of mechanical failure. It is shown that for any specific model the values of the Weibull shape parameters obtained may be strongly dependent on the degree of uncertainty of the underlying input parameters. Both the EP and PCG models can yield a wide range of values of β , from β >1, characteristic of wear-out behaviour, to β β =∞. In contrast, the shock-loading model is inherently random, and its predictions correspond closely to those of a constant hazard rate model, characterized by a value of β close to 1 for all finite degrees of parameter uncertainty. The results are discussed in the context of traditional methods for reliability analysis and conventional views on the nature of early-life failures.
Jan Kofron - One of the best experts on this subject based on the ideXlab platform.
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reverse Engineering Component models for quality predictions
Conference on Software Maintenance and Reengineering, 2010Co-Authors: Steffen Becker, Michael Hauck, Mircea Trifu, Klaus Krogmann, Jan KofronAbstract:Legacy applications are still widely spread. If a need to change deployment or update its functionality arises, it becomes difficult to estimate the performance impact of such modifications due to absence of corresponding models. In this paper, we present an extendable integrated environment based on Eclipse developed in the scope of the Q-Impress project for reverse Engineering of legacy applications (in C/C++/Java). The Q-Impress project aims at modeling quality attributes (performance, reliability, maintainability) at an architectural level and allows for choosing the most suitable variant for implementation of a desired modification. The main contributions of the project include i) a high integration of all steps of the entire process into a single tool, a beta version of which has been already successfully tested on a case study, ii) integration of multiple research approaches to performance modeling, and iii) an extendable underlying meta-model for different quality dimensions.
M Ristic - One of the best experts on this subject based on the ideXlab platform.
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monte carlo simulation and analysis of free form surface registration
Proceedings of the Institution of Mechanical Engineers. Part B. Journal of engineering manufacture, 1997Co-Authors: Djordje Brujic, M RisticAbstract:Accurate dimensional inspection and error analysis of free-form surfaces requires accurate regis- tration of the Component in hand. Registration of surfaces defined as non-uniform rational B-splines (NURBS) has been realized through an implementation of the iterative closest point method (ICP). The paper presents performance analysis of the ICP registration method using Monte Carlo simulation. A large number of simulations were performed on an example of a precision Engineering Component, an aero-engine turbine blade, which was judged to possess a useful combination of geometric characteristics such that the results of the analysis had generic significance. Data sets were obtained through CAD (computer aided design)-based inspection. Confidence intervals for estimated transformation parameters, maximum error between a measured point and the nominal surface (which is extremely important for inspection) mean error and several other performance criteria are presented. The influence of shape, number of measured points, measurement noise and some less obvious, but not less important, factors affecting confidence intervals are identified through statistical analysis.
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analysis of free form surface registration
International Conference on Image Processing, 1996Co-Authors: Djordje Brujic, M RisticAbstract:Registration of a rigid body is an essential step in many machine vision tasks. This paper presents performance analysis of the iterative closest point method implemented for dimensional inspection of free form surfaces where the set of measured points is registered to a NURBS model. The results were obtained through Monte Carlo simulation. A large number of experiments were performed on an example precision Engineering Component. Influences of shape, number of measured points and measurement noise on registration accuracy are identified through statistical analysis.
Philip W. L. Fong - One of the best experts on this subject based on the ideXlab platform.
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Title of thesis: Proof Linking: A Modular Verification Architecture
2008Co-Authors: Philip W. L. Fong, Simon Fraser UniversityAbstract:ii This dissertation presents a critical rethinking of the Java bytecode verification archi-tecture from the perspective of a software engineer. In existing commercial implemen-tations of the Java Virtual Machine, there is a tight coupling between the dynamic linking process and the bytecode verifier. This leads to delocalized and interleaving program plans, making the verifier difficult to maintain and comprehend. A modular mobile code verification architecture, called Proof Linking, is proposed. By establish-ing explicit verification interfaces in the form of proof obligations and commitments, and by careful scheduling of linking events, Proof Linking supports the construction of bytecode verifier as a separate Engineering Component, fully decoupled from Java’s dynamic linking process. This turns out to have two additional benefits: (1) Modu-larization enables distributed verification protocols, in which part of the verification burden can be safely offloaded to remote sites; (2) Alternative static analyses can now be integrated into Java’s dynamic linking process with ease, thereby making it conve-nient to extend the protection mechanism of Java. These benefits make Proof Linking a competitive verification architecture for mobile code systems. A prototype of the Proof Linking Architecture has been implemented in an open source Java Virtual Machine, the Aegis V
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Proof Linking: A Modular Verification Architecture for Mobile Code Systems
2004Co-Authors: Philip W. L. FongAbstract:This dissertation presents a critical rethinking of the Java bytecode verification architecture from the perspective of a software engineer. In existing commercial implementations of the Java Virtual Machine, there is a tight coupling between the dynamic linking process and the bytecode verifier. This leads to delocalized and interleaving program plans, making the verifier difficult to maintain and comprehend. A modular mobile code verification architecture, called Proof Linking, is proposed. By establishing explicit verification interfaces in the form of proof obligations and commitments, and by careful scheduling of linking events, Proof Linking supports the construction of bytecode verifier as a separate Engineering Component, fully decoupled from Java's dynamic linking process. This turns out to have two additional benefits: (1) Modularization enables distributed verification protocols, in which part of the verification burden can be safely offloaded to remote sites; (2) Alternative static analyses can now be integrated into Java's dynamic linking process with ease, thereby making it convenient to extend the protection mechanism of Java. These benefits make Proof Linking a competitive verification architecture for mobile code systems. A prototype of the Proof Linking Architecture has been implemented in an open source Java Virtual Machine, the Aegis VM (http://aegisvm.sourceforge.net). On th