Optimizing Asset Integrity for Critical Manufacturing Systems Using Advanced Proactive Maintenance Strategies
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Asset Integrity Management (AIM) is fundamental for optimizing asset performance by improving reliability, availability, maintainability, and safety (RAMS), while minimizing operational risks and costs. Failures in critical assets can result in substantial financial losses, safety hazards, and environmental consequences, highlighting the need for proactive maintenance strategies. This study introduces an innovative AIM framework that seamlessly integrates advanced technologies with proven methodologies to address these challenges. The framework combines Machine Learning (ML) for predictive analytics, enabling early fault detection, and Digital Twins (DT) for real-time asset monitoring and simulation. It also incorporates established approaches such as Risk-Based Inspection (RBI), Reliability-Centered Maintenance (RCM), Total Productive Maintenance (TPM), and Lean Six Sigma (LSS). This integration forms a holistic, datadriven approach to decision-making, operational optimization, risk reduction, and continuous improvement. A comprehensive literature review identifies critical gaps in traditional AIM practices, particularly the limited integration of emerging technologies and methodologies. The proposed framework bridges these gaps, enhancing asset performance, safety, and sustainability. This research highlights the transformative potential of combining advanced technologies with established AIM methodologies. It offers a strategic roadmap for industries to improve asset integrity, achieve operational excellence, and foster long-term sustainability. To the author’s knowledge, this is the first study to unify these six methodologies into a cohesive framework, providing valuable insights for implementing advanced maintenance strategies in complex industrial environments.
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