Abstract :

The Rapid Advancement Of Industry 4.0 Technologies Has Transformed Maintenance Management From Reactive And Preventive Approaches Toward Predictive And Intelligent Maintenance Systems. Traditional Maintenance Strategies Often Fail To Accurately Identify Emerging Equipment Failures, Resulting In Unplanned Downtime, Increased Operational Costs, And Safety Risks. Artificial Intelligence (AI), Combined With Reliability-Centered Maintenance (RCM) Principles, Offers A Promising Solution For Real-time Maintenance Risk Prediction And Failure Propagation Analysis. This Study Presents A Comprehensive Review And Conceptual Framework For Integrating AI-driven Predictive Analytics With RCM Methodologies To Enhance Maintenance Decision-making In Industrial Environments. A Systematic Literature Review Approach Was Adopted To Analyze Studies Published Between 2015 And 2026 Across Manufacturing, Energy, Transportation, Aviation, Healthcare, And Process Industries. The Review Examines Machine Learning, Deep Learning, Digital Twins, Internet Of Things (IoT), Edge Computing, And Explainable AI Applications In Maintenance Risk Assessment. The Findings Indicate That AI-based Predictive Maintenance Systems Can Reduce Maintenance Costs By 20– 40%, Decrease Equipment Downtime By Up To 50%, And Improve Asset Reliability By 25–35%. However, Challenges Related To Data Quality, Model Interpretability, Cybersecurity, Integration Complexity, And Organizational Readiness Remain Significant Barriers To Implementation. Based On The Analysis, A Conceptual Framework Is Proposed That Combines Real-time Sensor Monitoring, AI-based Risk Prediction, Propagation Modeling, And RCM Decision Logic. The Study Contributes To Theory By Integrating AI And RCM Perspectives, Provides Practical Guidance For Industrial Practitioners, And Identifies Future Research Directions Including Federated Learning, Autonomous Maintenance Systems, Sustainability-driven Maintenance Strategies, And Digital Twin-enabled Risk Propagation Analysis. The Proposed Framework Supports Organizations In Achieving Higher Operational Reliability, Resilience, And Sustainability In Increasingly Complex Industrial Systems.

Published:

22-12-2024

Issue:

Vol. 24 No. 12 (2024)

Page Nos:

318-329

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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