Predictive Maintenance and Equipment Monitoring Using AI

EasyChair Preprint 14175

Abstract

Predictive maintenance (PdM) and equipment monitoring have emerged as critical components in modern industrial operations, significantly enhancing reliability and efficiency. Traditional maintenance strategies—reactive and preventive—often fall short in addressing unforeseen equipment failures and optimizing maintenance schedules. The advent of Artificial Intelligence (AI) has revolutionized this field by offering advanced methodologies for predicting equipment failures and monitoring operational health.

This paper explores the integration of AI technologies in predictive maintenance and equipment monitoring. It begins with an overview of predictive maintenance principles and contrasts them with conventional approaches. The paper then delves into the components of predictive maintenance, including data collection, processing, and the application of AI models. Key AI techniques such as machine learning algorithms, deep learning models, and predictive analytics are examined for their role in analyzing sensor data, identifying patterns, and forecasting potential failures.

The discussion extends to various monitoring techniques including condition and performance monitoring, and anomaly detection. Real-world case studies across manufacturing, energy, and transportation sectors illustrate the practical benefits and outcomes of AI-driven predictive maintenance solutions. The paper also addresses challenges such as data quality, system integration, and cost considerations, providing insights into overcoming these obstacles.

Finally, the paper reflects on future trends, emphasizing advancements in AI technologies, the integration with emerging technologies like IoT and 5G, and evolving best practices. By leveraging AI, industries can achieve a paradigm shift in maintenance strategies, leading to reduced downtime, lower operational costs, and enhanced overall equipment effectiveness.

Keyphrases: Artificial Intelligence (AI), Data Analytics, Predictive Maintenance (PdM), deep learning, machine learning

@booklet{EasyChair:14175,
  author    = {Joseph Bayo Shalom},
  title     = {Predictive Maintenance and Equipment Monitoring Using AI},
  howpublished = {EasyChair Preprint 14175},
  year      = {EasyChair, 2024}}