Failure Lifetime Evaluation Based on Accelerated Generalized Wiener Degradation Process Models with Random Diffusion...

Area of Science:

• Reliability Engineering
• Materials Science
• Statistical Modeling

Background:

• Nonlinear degradation is a critical factor in product lifespan.
• Accelerated degradation testing (ADT) is vital for predicting long-term reliability.
• Existing models often struggle with complex degradation patterns.

Purpose of the Study:

• To develop a robust modeling framework for nonlinear degradation under constant-stress accelerated degradation testing (CSADT).
• To accurately predict product failure lifetime and reliability under normal operating conditions.
• To address the limitations of current models in capturing degradation heterogeneity.

Main Methods:

• Utilized a generalized Wiener process to model degradation with stress-dependent drift.
• Incorporated random effects to account for variability in diffusion coefficients and degradation trajectories.
• Employed the expectation-maximization (EM) algorithm for parameter estimation.
• Derived failure lifetime probability density function (PDF) and reliability function using the law of total probability.

Main Results:

• The proposed model demonstrated improved accuracy in predicting failure lifetime.
• Enhanced prediction of reliability function under normal operating conditions.
• Validation confirmed model efficacy using both simulated crack propagation and experimental wear data.

Conclusions:

• The developed modeling framework provides a more accurate approach to predicting product reliability.
• The model's ability to handle nonlinear degradation and heterogeneity makes it valuable for engineering applications.
• This research contributes to advancing predictive maintenance and product design through enhanced reliability analysis.