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The ARC Training Centre for
Transforming Maintenance through Data Science
Presentations

Comparing two Bayesian Hierarchical Models for Estimating Individual Failure Time Distributions from Inspection Data with Noise

gabriel-gonzalez

Virtual - Researchers Catch-up host online from Curtin University

Friday 20 October 2023

In maintenance and reliability engineering, assessing the performance and failure characteristics of systems is a critical task. This assessment often involves analysing individual failure time distributions obtained from inspection data. However, real-world inspection data is frequently contaminated with noise, leading to inaccurate conclusions when comparing these distributions. This presentation compares two Bayesian hierarchical approaches devised to address this challenge.

The studied methodologies leverage Bayesian statistics to model the individual failure time distributions and explicitly account for the noise in the inspection data. Doing so enables a more robust and accurate comparison of these distributions. The hierarchical nature of the approach allows for the incorporation of prior information and the borrowing of strength across different units or components, making it particularly useful when dealing with limited sample sizes or sparse data.

Gabriel will illustrate the effectiveness of his method through a case study. The results demonstrate the ability to enhance the reliability and precision of comparisons between individual failure time distributions in the presence of noisy inspection data, thereby providing valuable insights for decision-making and quality improvement.