Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Estimating degradation by a Wiener diffusion process subject to measurement error

G A Whitmore1

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

Lifetime Data Analysis
|January 1, 1995
PubMed
Summary

This study introduces a statistical model for material degradation, accounting for random degradation processes and measurement errors. The model uses a Wiener diffusion process to analyze degradation data, improving accuracy in engineering tests.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Patient Factors and Clinical Efficacy of Early Identification and Treatment of Chronic Obstructive Pulmonary Disease and Asthma.

American journal of respiratory and critical care medicine·2025
Same author

Cough in Adults with Undiagnosed Respiratory Symptoms.

Annals of the American Thoracic Society·2025
Same author

Assessment of Preserved Ratio Impaired Spirometry Using Pre- and Post-Bronchodilator Spirometry in a Randomly Sampled Symptomatic Cohort.

American journal of respiratory and critical care medicine·2023
Same author

Semiparametric predictive inference for failure data using first-hitting-time threshold regression.

Lifetime data analysis·2023
Same author

Patient and physician factors associated with symptomatic undiagnosed asthma or COPD.

The European respiratory journal·2022
Same author

Disease burden in individuals with symptomatic undiagnosed asthma or COPD.

Respiratory medicine·2022

Area of Science:

  • Materials Science
  • Statistical Modeling
  • Engineering Reliability

Background:

  • Materials and components degrade over time, a critical factor in engineering.
  • Existing degradation tests measure these processes but are affected by randomness and measurement errors.
  • Accurate measurement of degradation is essential for predicting component failure and ensuring reliability.

Purpose of the Study:

  • To develop a statistical model for measured degradation data.
  • To account for both inherent degradation randomness and measurement errors.
  • To provide a robust framework for analyzing degradation processes in engineering.

Main Methods:

  • A statistical model based on the Wiener diffusion process for degradation.
  • Assumption of independent normal random outcomes for measurement errors.

Related Experiment Videos

  • Development of inference procedures for the proposed statistical model.

    • Main Results:

    • The proposed model effectively incorporates both degradation variability and measurement error.
    • The Wiener diffusion process provides a suitable framework for modeling physical degradation.
    • The study outlines practical considerations for applying the statistical model to real-world data.

    Conclusions:

    • The developed statistical model offers a more accurate approach to analyzing material degradation data.
    • Accounting for both sources of variation enhances the reliability of engineering degradation tests.
    • The findings have implications for improving product lifespan prediction and quality control.