Published March 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Optimizing Asset Integrity for Critical Manufacturing Systems Using Advanced Proactive Maintenance Strategies

Creators

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

Description

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.

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Additional details

Identifiers

DOI
10.35940/ijese.B2026.13040325
EISSN
2319-6378

Dates

Accepted
2025-03-15
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.

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