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

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 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.

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

“…King et al [9] performed a comprehensive comparison between the traditional method and the parametric method. Xie et al [2] performed a comprehensive comparison among the three methods in term of TI estimation. For the model in (4), the TI is calculated as follows:…”

“…In literature, there are three methods available for ADDT data modeling and analysis: the traditional method based on the least-squares approach, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline models. The chapter in Xie et al [2] provides a comprehensive review for the three methods for ADDT data analysis and compares the corresponding TI estimation procedures via simulations.…”

A hidden-Gamma model-based filtering and prediction approach for monotonic health factors in manufacturing

“…A HF can be of very different nature: in its simplest form, HFs can be observable parameters that, thanks to specific domain expertise, can be associated with equipment/process health status. Example of health factors as quantities that are directly related to system health, such as the thermal index of a polymeric material [18], the scar width in sliding metal wear [19], and the temperature difference in semiconductor manufacturing epitaxy processes [20]. HFs can also be the output of Soft Sensor modules [21,22], where the status health is impossible/costly to be monitored.…”