Batch Variability in Accelerated-Degradation Testing

Li, Ming; Doganaksoy, Necip

doi:10.1080/00224065.2014.11917961

Cited by 14 publications

(16 citation statements)

References 6 publications

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“…In this section, we present a complete ADDT data analysis using the Seal Strength data to illustrate the use of functions in Section 2. The details of the Seal Strength data is available in Li and Doganaksoy [5]. The first ten observations are listed below.…”

Section: Discussionmentioning

confidence: 99%

“…In most applications, an ADDT dataset typically includes degradation measurements under different measuring time points, and accelerating variables such as temperature and voltage. In the ADDT package, there are four publicly available datasets ready for users to do analysis, which are the Adhesive Bond B data in [4], the Seal Strength data in [5], the Polymer Y data in [6], and the Adhesive Formulation K data in [7]. Users can load those datasets by downloading, installing the package ADDT and appropriately calling the data function.…”

Section: Datamentioning

confidence: 99%

“…The Adhesive Bond B data ( [4]) is used to do a step-by-step illustration. Section 3 provides a full analysis of the Seal Strength data ( [5]) so that users can see a typical ADDT modeling and analysis process. Section 4 contains some concluding remarks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data

Jin

Xie

Hong

et al. 2017

Statistical Modeling for Degradation Data

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Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparisons. The R package ADDT provides the functionalities of performing the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods for analyzing ADDT data, and then estimating the TI for polymeric materials. In this chapter, we provide a detailed introduction to the ADDT package. We provide a step-by-step illustration for the use of functions in the package. Publicly available datasets are used for illustrations.

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Section: Discussionmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

See 1 more Smart Citation

ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data

Jin

Xie

Hong

et al. 2017

Statistical Modeling for Degradation Data

View full text Add to dashboard Cite

show abstract

“…In statistical literature, parametric methods are prevalent in ADDT data analysis such as in [2], [3], and [4]. In the parametric method, the primary method for estimation and inference is based on a parametric model and maximum likelihood theory.…”

Section: The Parametric Methodsmentioning

confidence: 99%

“…Due to the nature of the testing, ADDT is a commonly used technique for evaluating long-term performance of polymeric materials. Examples of ADDT data includes the Adhesive Bond B data in [2], the Polymer data in [3], the Seal Strength data in [4], and the Formulation K data in [5].…”

Section: Introductionmentioning

confidence: 99%

Statistical Methods for Thermal Index Estimation Based on Accelerated Destructive Degradation Test Data

Xie

Jin

Hong

et al. 2017

Statistical Modeling for Degradation Data

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Accelerated destructive degradation test (ADDT) is a technique that is commonly used by industries to access material's long-term properties. In many applications, the accelerating variable is usually the temperature. In such cases, a thermal index (TI) is used to indicate the strength of the material. For example, a TI of 200 • C may be interpreted as the material can be expected to maintain a specific property at a temperature of 200 • C for 100,000 hours. A material with a higher TI possesses a stronger resistance to thermal damage. In literature, there are three methods available to estimate the TI based on ADDT data, which are the traditional method based on the least-squares approach, the parametric method, and the semiparametric method. In this chapter, we provide a comprehensive review of the three methods and illustrate how the TI can be estimated based on different models. We also conduct comprehensive simulation studies to show the properties of different methods. We provide thorough discussions on the pros and cons of each method. The comparisons and discussion in this chapter can be useful for practitioners and future industrial standards.

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A random‐effect gamma process model with random initial degradation for accelerated destructive degradation testing data

Ling

Bae

2023

Quality & Reliability Eng

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An accelerated degradation test (ADT) hastens degradation mechanisms of products by loading higher stresses than normal use conditions to shorten testing time. In some situations, degradation levels during ADT can be measured only by destructive inspection where testing units must be destroyed or physical characteristics are significantly changed after measuring the performance degradation. For such an accelerated destructive degradation test (ADDT), initial degradation levels may vary from item to item. In this regard, we propose a random‐effect gamma process model with random initial degradation for the reliability analysis of ADDT data. Under the proposed modeling framework, we derive the maximum likelihood estimates (MLEs) of the model parameters and construct an inferential procedure for the parameters and reliability measures of interest, using asymptotic properties of the MLEs. In particular, the mean and the variance of the mean‐time‐to‐failure of the products from the ADDT data are explicitly derived in closed forms. Monte Carlo simulations under various scenarios are performed to validate the performance of proposed maximum likelihood estimation and inferential methods for the ADDT data. Finally, reliability estimation at a normal use condition and inferential procedures are illustrated through an ADDT example of return‐springs in a bi‐functional DC motor system of an automobile.

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Batch Variability in Accelerated-Degradation Testing

Cited by 14 publications

References 6 publications

ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data

ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data

Statistical Methods for Thermal Index Estimation Based on Accelerated Destructive Degradation Test Data

A random‐effect gamma process model with random initial degradation for accelerated destructive degradation testing data

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