Development of Machine Learning Algorithms for Predictive Maintenance in Industrial Systems

Abstract

This paper presents a comprehensive study on the development of machine learning algorithms specifically designed for predictive maintenance applications. We explore various machine learning techniques, including supervised and unsupervised learning, to analyze operational data and predict equipment health status. Through extensive experiments on real-world datasets, we evaluate the performance of different algorithms in terms of accuracy, precision, and recall. Additionally, we discuss the integration of these algorithms within existing industrial frameworks and the potential impact on reducing downtime and maintenance costs. The findings demonstrate that machine learning not only enhances predictive capabilities but also contributes to a more efficient and sustainable industrial environment. Ultimately, this research aims to provide a foundational understanding of machine learning applications in PdM, paving the way for future advancements in industrial maintenance strategies.