Sensitivity Analysis of Optimal Designs for Accelerated Life Testing

Monroe, Eric M.; Pan, Rong; Anderson‐Cook, Christine M.; Montgomery, Douglas C.; Borror, Connie M.

doi:10.1080/00224065.2010.11917811

Cited by 28 publications

(7 citation statements)

References 13 publications

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“…This framing of the structure of the simulations to include influential factors that change the performance of the results allows for assessment of utility and robustness. There are starting to be more frequent use of this approach in many applications (for example, Monroe et al 36 ), but it should be more strongly encouraged as a research standard for presenting and comparing results.…”

Section: Designed Simulation Experimentsmentioning

confidence: 99%

Is designed data collection still relevant in the big data era?

Anderson‐Cook

2023

Quality & Reliability Eng

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The excitement and buzz around big data (BD) has been so prevalent that it seems BD has become thought of as a panacea for solving all challenges. But there are in fact limitations to what BD can do, which has led to missed opportunities and potentially suboptimal problem-solving and decision-making. Hence the answer to the question posed in the title is an emphatic 'Yes!' The intentional strategic use of designed data collection (DDC) represents an opportunity to elevate how studies involving BD are planned, executed, and interpreted to deliver more satisfying solutions. In this paper, we outline eleven opportunities for integrating DDC strategies, including statistical design of experiments (DoE) and sampling techniques, with BD at all stages of the study. There are opportunities for building background understanding of the problem and improving the quality of the data via strategic planning before acquiring BD. In addition, incorporating DDC into the collection and processing of the actual BD, and strengthening conclusions or augmenting with supplementary designed data after BD also represent key opportunities to increase the impact of BD in the era of Industry 4.0 and Quality 4.0.

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Section: Designed Simulation Experimentsmentioning

confidence: 99%

Is designed data collection still relevant in the big data era?

Anderson‐Cook

2023

Quality & Reliability Eng

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“…It was required that the selected design contributed to explain the issue of curvature in the response function since it implies a parabolic approach, which differs from the 2 k factorial, and, for this reason, the latter was not considered to be a good choice [24,25]. Furthermore, the 3 k factorial design is useful to investigate quadratic effects which are of interest in the present research [26]. It is worth mentioning that the interaction taken into account for the analysis of variance presented in this work was the one between and .…”

Section: Response Surface Analysis Of the Variables Andmentioning

confidence: 99%

Arsenic Removal from Natural Groundwater by Electrocoagulation Using Response Surface Methodology

García-Lara

Montero‐Ocampo

Equihua-Guillén

et al. 2014

Journal of Chemistry

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Contamination of natural groundwater by arsenic (As) is a serious problem that appears in some areas of Northern Central Mexico (NCM). In this research, As was removed from NCM wells groundwater by the electrocoagulation (EC) technique. Laboratory-scale arsenic electroremoval experiments were carried out at continuous flow rates between 0.25 and 1.00 L min−1using current densities of 5, 10, and 20 A m−2. Experiments were performed under galvanostatic conditions during 5 min, at constant temperature and pH. The response surface methodology (RSM) was used for the optimization of the processing variables (flow rate and current density), response modeling, and predictions. The highest arsenic removal efficiency from underground water (99%) was achieved at low flow rates (0.25 L min−1) and high current densities (20 A m−2). The response models developed explained 93.7% variability for As removal efficiency.

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“…At the same time, Yang pointed out that although optimum plans were statistically optimal, they were sensitive to the misspecification of planning information, because only 2 stress levels were used. Monroe et al proposed a generalized linear model (GLM) approach to design ALT plans and demonstrated the efficiency and robustness of the proposed plans. Studies including Yang and Pan and Pan et al also focused on designing ALT plans using the GLM approach.…”

Section: Introductionmentioning

confidence: 99%

Designing adaptive accelerated life tests using Bayesian methods

Xiang

Yang

Shen

2017

Quality & Reliability Eng

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The traditional constant‐stress accelerated life test may encounter the problem of no or a few failures at the low stress level, if the test is stopped after a fixed period. Insufficient failures usually make it difficult to estimate reliability characteristics and to discover design deficiencies of the product. To mitigate the problem, the adaptive plan is designed based on Bayesian methods in this study. Under the constraints of test facilities, test units, time, and cost, the adaptive plan is optimized according to the criterion of minimizing the preposterior variance of the logarithm of a quantile of the lifetime distribution at the use condition. Large‐sample approximation is applied to reduce the computational burden. Genetic algorithm is adopted to determine the optimal design of the adaptive plan. Finally, a numerical example is given to demonstrate the advantages of the proposed adaptive plan.

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Sensitivity Analysis of Optimal Designs for Accelerated Life Testing

Cited by 28 publications

References 13 publications

Is designed data collection still relevant in the big data era?

Is designed data collection still relevant in the big data era?

Arsenic Removal from Natural Groundwater by Electrocoagulation Using Response Surface Methodology

Designing adaptive accelerated life tests using Bayesian methods

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