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

IoT-Based Predictive Maintenance Models for Industrial Systems

Author(s) Van Phuc Do
Country France
Abstract IoT-based predictive maintenance has emerged as an important approach for improving the reliability, availability, and operational efficiency of industrial systems. Conventional maintenance strategies based on fixed schedules or reactive repairs may result in unnecessary maintenance activities, unexpected equipment failures, production interruptions, and inefficient resource utilization. The integration of Internet of Things sensors, industrial communication networks, cloud and edge computing, artificial intelligence, machine learning, and data analytics enables continuous equipment monitoring and data-driven maintenance decision-making. This study examines IoT-based predictive maintenance models for industrial systems, focusing on sensor-based condition monitoring, real-time data acquisition, anomaly detection, predictive analytics, failure forecasting, and maintenance optimization. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that IoT-enabled predictive maintenance can support continuous monitoring of equipment conditions and identify potential degradation before critical failure occurs.
Sensor data concerning vibration, temperature, pressure, acoustic emissions, current, and other operating parameters can provide valuable information for assessing equipment health. Machine-learning models can further identify patterns associated with equipment degradation and estimate potential failure conditions. However, implementation is affected by challenges including sensor deployment costs, data quality, interoperability, cybersecurity, model complexity, computational requirements, and workforce capabilities. The study concludes that IoT-based predictive maintenance models can provide a foundation for more intelligent and proactive industrial maintenance systems. Future research should focus on explainable predictive models, edge-based analytics, digital twins, adaptive learning, secure industrial IoT architectures, and scalable maintenance frameworks.
Keywords Internet of Things, Predictive Maintenance, Industrial Systems, Condition Monitoring, Machine Learning, Industrial IoT, Equipment Reliability, Predictive Analytics, Smart Manufacturing, Maintenance Optimization
Field Engineering
Published In Volume 4, Issue 4, July-August 2022
Published On 2022-07-14
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