Integration of Artificial Intelligence in Predictive Maintenance for Mechanical Systems

The growing complexity of modern mechanical systems demands advanced maintenance strategies that minimize downtime and operational costs. Traditional preventive maintenance is being replaced by predictive maintenance (PdM) driven by Artificial Intelligence (AI). AI algorithms, particularly those involving machine learning (ML) and deep learning (DL), can analyze real-time data from sensors to predict equipment failures before they occur. This paper explores the integration of AI in predictive maintenance for mechanical systems, focusing on key technologies, implementation frameworks, benefits, and challenges. A detailed review of AI-based condition monitoring, feature extraction, and predictive modeling techniques is presented. The study concludes that AI-driven predictive maintenance enhances reliability, optimizes maintenance schedules, and significantly reduces unplanned failures in industrial environments.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-14
DOI
https://doi.org/10.5281/zenodo.17609100
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integration of Artificial Intelligence in Predictive Maintenance for Mechanical Systems

Zenodo (CERN European Organization for Nuclear Research)
Machine Fault Diagnosis Techniques
article

Integration of Artificial Intelligence in Predictive Maintenance for Mechanical Systems

article en

Abstract

The growing complexity of modern mechanical systems demands advanced maintenance strategies that minimize downtime and operational costs. Traditional preventive maintenance is being replaced by predictive maintenance (PdM) driven by Artificial Intelligence (AI). AI algorithms, particularly those involving machine learning (ML) and deep learning (DL), can analyze real-time data from sensors to predict equipment failures before they occur. This paper explores the integration of AI in predictive maintenance for mechanical systems, focusing on key technologies, implementation frameworks, benefits, and challenges. A detailed review of AI-based condition monitoring, feature extraction, and predictive modeling techniques is presented. The study concludes that AI-driven predictive maintenance enhances reliability, optimizes maintenance schedules, and significantly reduces unplanned failures in industrial environments.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 42%
Machine Fault Diagnosis Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.