Abstract
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 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 h. 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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Acknowledgements
The authors thank William Q. Meeker for his helpful comments on earlier version of the paper. The authors acknowledge Advanced Research Computing at Virginia Tech for providing computational resources. The work by Hong was partially supported by the National Science Foundation under Grant CMMI-1634867 to Virginia Tech.
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Xie, Y., Jin, Z., Hong, Y., Van Mullekom, J.H. (2017). Statistical Methods for Thermal Index Estimation Based on Accelerated Destructive Degradation Test Data. In: Chen, DG., Lio, Y., Ng, H., Tsai, TR. (eds) Statistical Modeling for Degradation Data. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5194-4_12
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DOI: https://doi.org/10.1007/978-981-10-5194-4_12
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