American International Journal of Advanced Research and Development

E-ISSN: XXXX-XXXX   •   Impact Factor: -

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 5 (September-October 2026) Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 5
September-October 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Intelligent Maintenance Systems for Industrial Equipment Management

Author(s) Prof. Benoît Iung
Country France
Abstract Intelligent maintenance systems are increasingly important for managing complex industrial equipment because conventional maintenance approaches often depend on fixed schedules, manual inspection, and reactive intervention. The integration of artificial intelligence, machine learning, industrial Internet of Things technologies, condition monitoring, and predictive analytics enables maintenance decisions to be supported by continuously generated equipment data. This paper examines the role of intelligent maintenance systems in industrial equipment management through a literature-based conceptual analysis.
The study focuses on condition monitoring, fault detection, predictive maintenance, remaining useful life estimation, data-driven decision support, and operational challenges. Existing literature indicates that intelligent maintenance can improve equipment reliability, maintenance planning, resource utilization, and production continuity when appropriate data and analytical models are available. However, implementation remains constrained by data quality, model interpretability, cybersecurity, integration complexity, and changing operating conditions.
The paper identifies major challenges and proposes future research directions involving explainable artificial intelligence, digital twins, hybrid models, edge computing, and human-centered maintenance decision systems. Recent reviews similarly emphasize the importance of integrating machine learning, intelligent sensing, and Industry 4.0 technologies into maintenance environments.
Keywords : Intelligent Maintenance Systems, Industrial Equipment, Predictive Maintenance, Artificial Intelligence, Machine Learning, Condition Monitoring, Industrial IoT, Equipment Management.
Field Engineering
Published In Volume 4, Issue 1, January-February 2022
Published On 2022-01-05
View / Download PDF File

Share this



E-ISSN XXXX-XXXX
CrossRef

CrossRef DOI prefix of AIJARD is 10.00000/AIJARD

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJARD
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +1-872-302-3851 editor@aijard.com