A General Simulation-Based Optimisation Framework for Multipoint Constant-Stress Accelerated Life Tests

TL;DR

This paper presents a flexible simulation-based framework using differential evolution to optimize multipoint constant-stress accelerated life tests, improving test plan design for better lifetime prediction accuracy.

Contribution

It introduces a general simulation approach for optimizing complex ALT test plans, especially when analytical solutions are infeasible.

Findings

Optimal number of stress levels relates to model parameters.

Test unit allocation inversely proportional to stress level.

Framework effectively handles large, complex test plans.

Abstract

Accelerated life testing (ALT) is a method of reducing the lifetime of components through exposure to extreme stress. This method of obtaining lifetime information involves the design of a testing experiment, i.e., an accelerated test plan. In this work, we adopt a simulation-based approach to obtaining optimal test plans for constant-stress accelerated life tests with multiple design points. Within this simulation framework we can easily assess a variety of test plans by modifying the number of test stresses (and their levels) and evaluating the allocation of test units. We obtain optimal test plans by utilising the differential evolution (DE) optimisation algorithm, where the inputs to the objective function are the test plan parameters, and the output is the RMSE (root mean squared error) of out-of-sample (extrapolated) model predictions. When the life-stress distribution is correctly specified, we show that the optimal number of stress levels is related to the number of model parameters. In terms of test unit allocation, we show that the proportion of test units is inversely related to the stress level. Our general simulation framework provides an alternative approach to theoretical optimisation, and is particularly favourable for large/complex multipoint test plans where analytical optimisation could prove intractable. Our procedure can be applied to a broad range of experimental scenarios, and serves as a useful tool to aid practitioners seeking to maximise component lifetime information through accelerated life testing.