Published April 30, 2025 | Version CC-BY-NC-ND 4.0
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RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems

Creators

  • 1. Department of Mechanical Engineering, Faculty of Engineering, Shubra, Benha University, Cairo, Egypt.

Description

Abstract: Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maintenance paradigm, transforming reactive and preventive approaches into autonomous, data-driven ecosystems that enhance operational efficiency and resilience. The proposed framework synergizes RCM principles with Lean Six Sigma’s DMAIC (Define-Measure-Analyze-Improve-Control) methodology, providing a structured, data-driven approach to failure mode classification, risk-based maintenance prioritization, and real-time performance optimization. By leveraging IIoTenabled condition monitoring, Digital Twin simulations, and machine learning-driven predictive analytics, RCM 4.0 enables real-time anomaly detection, intelligent diagnostics, and adaptive maintenance strategies. This shift eliminates inefficiencies, minimizes downtime, optimizes asset performance, and enhances cost-effective maintenance planning. This research establishes RCM 4.0 as a transformative approach to industrial maintenance, integrating cyber-physical intelligence to drive operational resilience, sustainability, and cost efficiency. Future research will explore 5G-enabled industrial communication, autonomous robotic maintenance, blockchain-secured predictive maintenance, and edge AI-powered diagnostics, further advancing nextgeneration digitalized maintenance ecosystems' scalability, cybersecurity, and self-learning capabilities.

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Additional details

Identifiers

DOI
10.35940/ijese.E2595.13050425
EISSN
2319-6378

Dates

Accepted
2025-04-15
Manuscript received on 10 March 2025 | First Revised Manuscript received on 11 March 2025 | Second Revised Manuscript received on 22 March 2025 | Manuscript Accepted on 15 April 2025 | Manuscript published on 30 April 2025.

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