RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems

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 IIoT enabled 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 next generation digitalized maintenance ecosystems' scalability, cybersecurity, and self-learning capabilities.