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.
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Gabriel Gonzalez presents - Comparing two Bayesian Hierarchical Models for Estimating Individual Failure Time Distributions from Inspection Data with Noise at the next researcher catchup..
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.
When - Friday 20 October at 1.30pm AWST
Where - Curtin University - Building 216 - Level 2 Innovation Central or online via Microsoft Teams meeting - Click here to join the meeting
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