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Modelling accelerated degradation data using Wiener diffusion with a time scale transformation

G A Whitmore1, F Schenkelberg

  • 1Faculty of Management, McGill University, Montreal, Quebec, Canada.

Lifetime Data Analysis
|January 1, 1997
PubMed
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This study introduces a statistical model using Wiener diffusion to predict the lifespan of long-life products under accelerated degradation testing. It enables accurate lifetime predictions and degradation assessments for industrial applications.

Area of Science:

  • Engineering
  • Statistics
  • Reliability Engineering

Background:

  • Assessing the lifespan of long-life products is crucial for industry.
  • Traditional life tests are often insufficient for products that degrade slowly.
  • Accelerated degradation testing under high stress is a common industry practice.

Purpose of the Study:

  • To present a general statistical model for equipment performance degradation.
  • To incorporate Arrhenius extrapolation for high-stress testing within the model.
  • To define and predict product lifetime based on a failure threshold.

Main Methods:

  • Utilizing a Wiener diffusion process with time scale transformation to model degradation.
  • Applying Arrhenius extrapolation for accelerated life testing analysis.

Related Experiment Videos

  • Developing inference methods for model parameters using degradation test data.

    • Main Results:

    • The presented model effectively describes performance degradation over time.
    • The model successfully predicts product lifetime and future degradation levels.
    • Case application with self-regulating heating cables demonstrates model utility.

    Conclusions:

    • The statistical model provides a robust framework for analyzing accelerated degradation data.
    • The methods allow for reliable prediction of product lifespan and performance.
    • Practical application issues in degradation testing are addressed.