FUSION OF VIBRATION, ULTRASOUND, AND TEMPERATURE FOR OFFLINE SMART PREDICTIVE MAINTENANCE ON OIL AND GAS PLATFORMS ROTATING EQUIPMENT: A PRELIMINARY MATHEMATICAL FRAMEWORK

Amr Y. Menisy, Mohamed M. ElGohary, Ahmed Al-Kabbany, Ahmed S. Shehata

Abstract


We present a deployment-oriented, offline condition-monitoring framework for rotating equipment on oil and gas platforms that fuses vibration, ultrasound, and temperature signals into a single, interpretable health index. The workflow verifies cross-sensor consistency via Pearson correlation, applies z-score normalization, and performs feature-level fusion before classifying equipment states with transparent, ISO-referenced thresholds (Normal / Alarm / Danger). Tailored to offshore constraints where continuous telemetry is impractical, the framework emphasizes low-cost periodic acquisition and auditable rules over black-box models, while remaining ready for subsequent AI/ML integration. On real platform data, the approach improved maintenance KPIs (availability/reliability/cost) by approximately 15–20% versus conventional strategies. This contribution is, to our knowledge, the first offline, multi-sensor fusion framework purpose-built for oil & gas platforms that couples correlation-validated fusion with thresholded, interpretable decisions. 

Keywords


Predictive Maintenance (PdM), Rotating Equipment, Industry 4.0, Artificial Intelligence, Machine learning, Condition Monitoring (CM), Oil and Gas platforms

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DOI: https://dx.doi.org/10.21622/MARLOG.2026.15.1.55

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The International Maritime Transport and Logistics Journal (MARLOG)

E-ISSN: 2974-3141
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