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

TL;DR

This paper reviews statistical methods for estimating the thermal index from accelerated destructive degradation test data, comparing traditional, parametric, and semiparametric approaches through simulations.

Contribution

It provides a comprehensive comparison and discussion of three methods for thermal index estimation, aiding practitioners and standard development.

Findings

Semiparametric method shows flexible modeling advantages.

Simulation results highlight differences in accuracy and robustness.

Discussion informs best practices for industry applications.

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 200C may be interpreted as the material can be expected to maintain a specific property at a temperature of 200C 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.