A Lightweight Machine Learning Framework Predictive Maintenance and Operational Safety in Offshore Energy Systems

Abstract

Mechanical failures in critical offshore components, such as pumps and compressors, constitute a major challenge and can contribute non-productive causes to the field in deepwater operations, estimated to account for up to 20% of total downtime, with substantial production and environmental impacts. Existing data-driven predictive maintenance solutions are often computationally intensive and ill-suited for operators with limited digital infrastructure. To address these limitations, we developed a novel, lightweight predictive maintenance framework that combines gradient-boosted decision trees (LightGBM) with real-time anomaly detection to forecast equipment failure risks in resource-constrained offshore environments. This solution, called OffshoreGuard, is validated on the NASA C-MAPSS FD001 benchmark dataset and integrates a prototype dashboard with a Large Language Model (LLM) interface to enable natural-language querying and automated maintenance report generation. The framework achieved a risk classification F1 score of 86.58% at 92.29% precision, a RUL estimation RMSE of 18.42 cycles with a relative error of 18.55%, and an anomaly detection F1 score of 73.39%. With a total serialised model footprint of 1.6 MB, OffshoreGuard is deployable on standard web infrastructure at zero licensing cost, offering an affordable and scalable solution for asset managers in the West African energy sector to enhance operational safety, reduce non-productive time, and extend equipment life cycles.