Aging Intensity for Step-Stress Accelerated Life Testing Experiments

Area of Science:

• Reliability Engineering
• Statistical Modeling

Background:

• Aging intensity (AI) quantifies reliability properties of lifetimes using hazard rate ratios.
• Step-stress accelerated life testing (SSALT) is crucial for product reliability assessment under varying stress levels.

Purpose of the Study:

• Introduce the concept of aging intensity (AI) within SSALT experiments.
• Clarify the distinctions between cumulative exposure (CE) and tampered failure rate (TFR) models using AI.
• Develop and evaluate novel AI-based estimators for SSALT model parameters.

Main Methods:

• Definition of aging intensity (AI) as the ratio of instantaneous to baseline hazard rates.
• Application of AI to step-stress accelerated life testing (SSALT) framework.
• Development of AI-based estimators and comparison with Maximum Likelihood Estimators (MLEs).

Main Results:

• AI provides a novel perspective for analyzing SSALT data.
• The study elucidates the differences between CE and TFR models through the AI lens.
• AI-based estimators demonstrate comparable or improved performance against MLEs in simulation studies.

Conclusions:

• Aging intensity is a valuable tool for enhancing the understanding and analysis of SSALT experiments.
• The proposed AI-based estimators offer a robust alternative for parameter estimation in SSALT.
• This research contributes to more accurate reliability predictions in accelerated testing environments.