Reliability improvement through designed experiments with random effects
Computers & Industrial Engineering, ISSN: 0360-8352, Vol: 112, Page: 231-237
2017
- 18Citations
- 23Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Design of experiment (DOE) is a useful tool to identify significant factors and choose factor levels for product reliability improvement. In practice, practitioners often ignore random effects that result from the experimental protocol in the reliability experiment. In this paper, we consider product reliability improvement with designed experiment when the test is actually not completely randomized. The Weibull distribution is used to model the lifetime, leading to a smallest extreme value distribution for the log-lifetime. Random effects are incorporated into the model through mean time to failure (MTTF). We improve the product reliability with maximizing the MTTF. The simulation study shows that ignoring random effects in modeling can result in unreliable factors identification and estimation. We also illustrate the proposed method with a real example.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0360835217303297; http://dx.doi.org/10.1016/j.cie.2017.07.027; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85028499847&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0360835217303297; https://dx.doi.org/10.1016/j.cie.2017.07.027
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know