Enhancing Predictive Maintenance in Manufacturing Using Machine Learning Algorithms and IoT-Driven Data Analytics

Abstract

This research paper explores the integration of machine learning algorithms with Internet of Things (IoT)-driven data analytics to enhance predictive maintenance in the manufacturing sector. The study addresses the increasing demand for efficient maintenance strategies that minimize downtime and optimize operational efficiency. Through a comprehensive analysis, the research identifies key machine learning models—such as random forests, support vector machines, and neural networks—best suited for predictive maintenance tasks. IoT devices facilitate real-time data acquisition from manufacturing equipment, enabling continuous monitoring and early fault detection. The paper discusses the architecture of an IoT-enabled predictive maintenance system, emphasizing the roles of data preprocessing, feature selection, and model training in achieving high prediction accuracy. A case study is presented where these techniques were applied in a manufacturing facility, resulting in a 30% reduction in unexpected equipment downtime and a 20% decrease in maintenance costs. The findings demonstrate the practical benefits of integrating IoT and machine learning, offering a scalable solution for manufacturers seeking to transition from reactive to predictive maintenance models. The paper concludes by highlighting the challenges and future research directions, including data privacy concerns, model interpretability, and the incorporation of emerging technologies such as edge computing.