Sandy Spierspresented - Understanding the Difficulties of Optimisation
Within the architecture of an optimisation solution, the algorithm component often goes misunderstood or neglected, yet this is where most of the heavy lifting is done.
Sandy presented that a way to remedy this by unpacking what actually makes an optimisation problem difficult. He explored the idea of decision dimensions and explained why some problems are far more complex than others.
By demystifying some common optimisation algorithms, Sandy presented how practical assumptions lead to more intuitive algorithms, providing a more holistic understanding of optimisation solutions.
Gabriel Gonzalezcompared two Bayesian Hierarchical Models for Estimating Individual Failure Time Distributions from Inspection Data with Noise
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. Gabriel 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 illustrated 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.
New Publications October 2023
Stay tuned for our next issue where we will cover:
CTMTDS Newsletter 21, October 2023
This month's newsletter focuses on the activities in October
IN THE SPOTLIGHT
Dr Sirui Li attends her graduation ceremony at Murdoch University. Congratulations Sirui
TEAM NEWS
We welcomed two new members to our Strategic Risk and Reporting Board.
Peter Rose joins the board to represent Alcoa
Johan Breytenbach joins the board to represent Roy Hill
INTERNATIONAL CONFERENCES
Events
Researcher Catch-up October 2023
Shuixiu Lu hosts research presentations by PhD candidates Sandy Spiers and Gabriel Gonzalez
Sandy Spiers presented - Understanding the Difficulties of Optimisation
Within the architecture of an optimisation solution, the algorithm component often goes misunderstood or neglected, yet this is where most of the heavy lifting is done.
Sandy presented that a way to remedy this by unpacking what actually makes an optimisation problem difficult. He explored the idea of decision dimensions and explained why some problems are far more complex than others.
By demystifying some common optimisation algorithms, Sandy presented how practical assumptions lead to more intuitive algorithms, providing a more holistic understanding of optimisation solutions.
Gabriel Gonzalez compared two Bayesian Hierarchical Models for Estimating Individual Failure Time Distributions from Inspection Data with Noise
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. Gabriel 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 illustrated 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.
New Publications October 2023
Stay tuned for our next issue where we will cover:
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