Optimizing Asset Integrity for Critical Manufacturing Systems Using Advanced Proactive Maintenance Strategies
Attia Hussien Gomaa
Prof. Dr. Attia Hussien Gomaa, Department of Mechanical Engineering, Faculty of Engineering. Shubra, Benha University, Cairo, Egypt.
Manuscript Received on 21 January 2025 | First Revised Manuscript Received on 31 January 2025 | Second Revised Manuscript Received on 16 February 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 21-33 | Volume-13 Issue-4, March 2025 | Retrieval Number: 100.1/ijese.B202605020225 | DOI: 10.35940/ijese.B2026.13040325
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Asset Integrity Management, Proactive Maintenance, Risk-Based Inspection, Reliability-Centered Maintenance, Total Productive Maintenance, Lean Six Sigma, Machine Learning, Digital Twin.
Scope of the Article: Artificial Intelligence and Methods