Unlocking the Power of Machine Learning in Maintenance Optimization: A Case Study on Rotating Equipment in Industries

Abstract

Rotating pumps are crucial components in various industrial processes, and their failure can lead to significant downtime and maintenance costs. Machine learning (ML) has emerged as a promising approach to enhance maintenance optimization by predicting equipment failures and reducing maintenance costs. This study explores the application of machine learning techniques for the predictive maintenance of rotating pumps. The study evaluated the performance of Decision Trees, Random Forests, and Support Vector Machines using a comprehensive dataset and compare their accuracy, precision, and recall. The result showed that Random Forest achieves the highest accuracy and robustness, making it a suitable choice for real-world applications. This research contributes to the existing body of knowledge by providing a comparative analysis of machine learning models for predictive maintenance and highlighting the importance of hyperparameter tuning and data preprocessing. The findings of this study can help industries optimize maintenance strategies, reduce downtime, and enhance overall efficiency.