Abstract
With its proactive approach to equipment maintenance, predictive maintenance is at the forefrontof industrial innovation, significantly cutting operational costs and downtime. This report explores machine learning (ML) algorithms, such as decision trees, neural networks, random forests, and support path machines, about predictive maintenance systems. By reliably anticipating equipment problems before they occur, these algorithms have the potential to revolutionise maintenance practices. Nevertheless, there are challenges in implementing them. Each method’s intrinsic strengths and weaknesses, dealing with data quality issues, and ensuring model interpretability are vital barriers. This report highlights existing research gaps and suggests future approaches to improve the efficacy of predictive maintenance systems after conducting a thorough assessment and analysis. The ultimate objective is to use the knowledge gathered from this study to provide cutting-edge solutions that address these challenges, opening the way for more dependable and effective maintenance methods in various sectors.
Predictive Maintenance Systems: A Comparative Analysis of Machine Learning Algorithms
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Predictive Maintenance Systems: A Comparative Analysis of Machine Learning Algorithms
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