Leveraging AI and Machine Learning for Predictive Maintenance in Manufacturing

EasyChair Preprint 14550

Abstract

The advent of Industry 4.0 has significantly transformed the manufacturing sector, with predictive maintenance (PdM) emerging as a crucial element for optimizing operational efficiency and reducing downtime. This paper presents a novel AI-driven predictive maintenance framework that leverages machine learning (ML) models to predict equipment failures before they occur. By integrating big data analytics and cloud computing, the proposed solution enhances the accuracy and scalability of predictive maintenance strategies. Various ML models, including Gradient Boosting Machines, Neural Networks, and Support Vector Machines, are evaluated using a comprehensive manufacturing dataset. The results demonstrate the efficacy of AI in improving predictive accuracy and reducing maintenance costs, thereby driving significant operational benefits for manufacturers. A comparative analysis with existing literature further highlights the superior performance of the proposed framework.

Keyphrases: Artificial Intelligence, Big Data, Cloud Computing, Industry 4.0, Manufacturing, Predictive Maintenance, machine learning

@booklet{EasyChair:14550,
  author    = {Anastasia Ivanov},
  title     = {Leveraging AI and Machine Learning for Predictive Maintenance in Manufacturing},
  howpublished = {EasyChair Preprint 14550},
  year      = {EasyChair, 2024}}