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Thomas, Edward V.

Publications and source records attributed to Thomas, Edward V..

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Modeling memoryless degradation under variable stress

Accelerated degradation tests can be used as the basis for predicting the performance or state of health of products and materials at use conditions over time. Measurements acquired at accelerated levels of stress are used to develop models that relate to the degradation of one or more performance measures. Frequently, products/materials of interest are subjected to variable stress levels during their lifetimes. However, testing is usually performed only at a few fixed stress levels. In such cases, cumulative degradation models are developed and assessed by using data acquired under those fixed stress conditions. The degradation rate at any stress condition within the range of the model can be estimated by the derivative of the cumulative model at that stress condition. It follows that, to predict cumulative degradation over variable use conditions, one might integrate the fluctuating degradation rate over time. Existing approaches for doing this consider degradation rates that depend only on the current stress level. Here, we propose to allow the degradation rate to also depend on the current state of health as indicated by the associated performance measure(s). The resulting modeling approach is capable of portraying a broader range of degradation behavior than existing approaches. The assertion of memoryless degradation by using this or any other approach should be assessed experimentally with data acquired under variable stress in order to increase confidence that the integrated rate model is accurate. In this article, we demonstrate the additional capability of the proposed approach by developing empirical memoryless rate-based degradation models to predict resistance increase and capacity decrease in lithium-ion cells that are being evaluated for use in electric vehicles. Here, we then assess the plausibility of these models.

42 ENGINEERING↗
DOE OSTI2017

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factors and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.

42 ENGINEERING↗
DOE OSTI2017

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictates the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Construction of tolerance bounds for a multivariate response associated with a covariate: A case study

The purpose of this research is to describe and illustrate practical methods that can be used to construct tolerance bounds for a multivariate measurement associated with a covariate. These methods rely on principal components analysis and the parametric bootstap. The methods are illustrated with an example in which the vibration environment experienced by a test object being carried by an aircraft (known as captive carry) is characterized.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Design of experiments and data analysis challenges in calibration for forensics applications

Forensic science aims to infer characteristics of source terms using measured observables. Our focus is on statistical design of experiments and data analysis challenges arising in nuclear forensics. More specifically, we focus on inferring aspects of experimental conditions (of a process to produce product Pu oxide powder), such as temperature, nitric acid concentration, and Pu concentration, using measured features of the product Pu oxide powder. The measured features, Y, include trace chemical concentrations and particle morphology such as particle size and shape of the produced Pu oxide power particles. Making inferences about the nature of inputs X that were used to create nuclear materials having particular characteristics, Y, is an inverse problem. Therefore, statistical analysis can be used to identify the best set (or sets) of Xs for a new set of observed responses Y. One can fit a model (or models) such as Υ = f(Χ) + error , for each of the responses, based on a calibration experiment and then “invert” to solve for the best set of Xs for a new set of Ys. This perspectives paper uses archived experimental data to consider aspects of data collection and experiment design for the calibration data to maximize the quality of the predicted Ys in the forward models; that is, we assume that well-estimated forward models are effective in the inverse problem. In addition, we consider how to identify a best solution for the inferred X, and evaluate the quality of the result and its robustness to a variety of initial assumptions, and different correlation structures between the responses. In addition, we also briefly review recent advances in metrology issues related to characterizing particle morphology measurements used in the response vector, Y.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Solving Inverse Radiation Transport Problems with Multi-Sensor Data in the Presence of Correlated Measurement and Modeling Errors

Inverse radiation transport focuses on identifying the configuration of an unknown radiation source given its observed radiation signatures. The inverse problem is traditionally solved by finding the set of transport model parameter values that minimizes a weighted sum of the squared differences by channel between the observed signature and the signature predicted by the hypothesized model parameters. The weights are inversely proportional to the sum of the variances of the measurement and model errors at a given channel. The traditional implicit (often inaccurate) assumption is that the errors (differences between the modeled and observed radiation signatures) are independent across channels. Here, an alternative method that accounts for correlated errors between channels is described and illustrated using an inverse problem based on the combination of gamma and neutron multiplicity counting measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

A Statistical Perspective on Highly Accelerated Testing

Highly accelerated life testing has been heavily promoted at Sandia (and elsewhere) as a means to rapidly identify product weaknesses caused by flaws in the product's design or manufacturing process. During product development, a small number of units are forced to fail at high stress. The failed units are then examined to determine the root causes of failure. The identification of the root causes of product failures exposed by highly accelerated life testing can instigate changes to the product's design and/or manufacturing process that result in a product with increased reliability. It is widely viewed that this qualitative use of highly accelerated life testing (often associated with the acronym HALT) can be useful. However, highly accelerated life testing has also been proposed as a quantitative means for "demonstrating" the reliability of a product where unreliability is associated with loss of margin via an identified and dominating failure mechanism. It is assumed that the dominant failure mechanism can be accelerated by changing the level of a stress factor that is assumed to be related to the dominant failure mode. In extreme cases, a minimal number of units (often from a pre-production lot) are subjected to a single highly accelerated stress relative to normal use. If no (or, sufficiently few) units fail at this high stress level, some might claim that a certain level of reliability has been demonstrated (relative to normal use conditions). Underlying this claim are assumptions regarding the level of knowledge associated with the relationship between the stress level and the probability of failure. The primary purpose of this document is to discuss (from a statistical perspective) the efficacy of using accelerated life testing protocols (and, in particular, "highly accelerated" protocols) to make quantitative inferences concerning the performance of a product (e.g., reliability) when in fact there is lack-of-knowledge and uncertainty concerning the assumed relationship between the stress level and performance. In addition, this document contains recommendations for conducting more informative accelerated tests.

42 ENGINEERING↗