Thesis
Maintenance Optimization for Latent Degradation Systems
Masters by Research, Murdoch University
2024
Abstract
Few challenges within industry are as ubiquitous as the costs involved in degradation and repair. Whether it’s maintaining an assembly line, replacing a light bulb, or calibrating equipment, maintenance is an ongoing necessity. Consequently, maintenance is often one of the largest source of costs that many industries must deal with. To address this issue, various strategies have been developed to guide maintenance decisions, aiming to minimise costs while maximizing the lifespan of equipment. Common strategies include those based on a system’s age, schedule maintenance and observable conditions.
Of the relevant strategies, Condition-Based Maintenance (CBM) stands out for its cost-effectiveness, making it the preferred choice in many industrial settings. However, its reliance on observable conditions significantly restricts its applicability. In realworld scenarios, breakdowns often occur with no prior warning, rendering CBM less reliable. These sudden breakdowns are typically caused by the accumulation of latent damages within the system, a phenomenon known as latent degradation. Since this degradation is not directly observable, analysts rely on characteristics associated with system performance, referred to as markers, to infer the system’s latent condition. Previous studies have explored this type of degradation through a CBM framework, using a bivariate gamma process model with perfect repair actions. In this study, we build upon thesis work by incorporating imperfect repair actions into a bivariate gamma process model to minimise maintenance cost by strategically determining optimal key decision variables.
Details
- Title
- Maintenance Optimization for Latent Degradation Systems
- Authors/Creators
- Connor Stewart-Green
- Contributors
- Soudabeh Shemehsavar (Supervisor) - Murdoch University, College of Science, Technology, Engineering and MathematicsGraeme Hocking (Supervisor) - Murdoch University, Centre for Animal Production and Health
- Awarding Institution
- Murdoch University; Masters by Research
- Identifiers
- 991005743942907891
- Murdoch Affiliation
- School of Mathematics, Statistics, Chemistry and Physics
- Resource Type
- Thesis
- Note
- Accelerated Research Masters with Training
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