AI-Powered Predictive Maintenance in Manufacturing: Enhancing Equipment Reliability and Reducing Downtime

EasyChair Preprint 15383

Abstract

Predictive maintenance (PdM) powered by artificial intelligence (AI) is transforming the manufacturing industry by enabling proactive identification of potential equipment failures. By leveraging machine learning algorithms on equipment data, AI-based PdM can accurately forecast maintenance needs, reducing unplanned downtime and maintenance costs. This paper explores the implementation of AI-powered predictive maintenance in manufacturing, focusing on techniques such as anomaly detection, time-series analysis, and deep learning models. Case studies from various industries demonstrate PdM’s effectiveness in enhancing equipment reliability, minimizing disruptions, and optimizing maintenance schedules, thus driving operational efficiency.

Keyphrases: Artificial Intelligence, Manufacturing, Predictive Maintenance, anomaly detection, downtime reduction, equipment reliability, machine learning

@booklet{EasyChair:15383,
  author    = {Lucas Zhang},
  title     = {AI-Powered Predictive Maintenance in Manufacturing: Enhancing Equipment Reliability and Reducing Downtime},
  howpublished = {EasyChair Preprint 15383},
  year      = {EasyChair, 2024}}