Aug 2026· SPE Nigeria Annual International Conference and Exhibition· 0 citations· 26 references
TL;DR
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 and is deployable on standard web infrastructure at zero licensing cost.
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.
Electric Submersible Pumps (ESP) are among the most widely used artificial lift methods in oil production wells. Despite their effectiveness, ESP systems are prone to unexpected failures that lead to significant production losses, costly workover operations, and extended downtime. Traditional maintenance strategies, such as corrective or time-based maintenance, are often insufficient in predicting early signs of ESP degradation. This paper presents a machine learning–based approach for predicting potential ESP failures using historical SCADA operational data. The framework integrates statistical feature engineering via a sliding window, SMOTE-based class balancing, and a comparative evaluation of four algorithms: Random Forest, XGBoost (XGB), Extra Trees, and Logistic Regression. The results demonstrate that tree-based ensemble models achieve exceptional classification accuracy up to 99.96%, significantly outperforming the baseline Logistic Regression model (99.83%) and effectively identifying early indicators of ESP performance degradation with minimal misclassifications. Feature importance evaluation revealed that thermal and hydraulic parameters serve as the primary diagnostic anchors. Integrating this predictive framework into automated systems can successfully transition field operations from reactive to proactive maintenance, optimizing the economic lifecycle of ESP systems.
W. Mahmud, Masara A. Aldowro· Global Journal of Energy Tec...· 0 citations
Electrified transportation is becoming a crucial component of the global energy transition. Therefore, ensuring the reliability and resilience of its subsystems is essential for its sustainable operations. The most important subsystem is the battery system whose failures can affect operational efficiency. While the machine learning predictive maintenance method has shown promise for early fault detection, most existing approaches rely on offline and statically trained models that are unable to adapt to sensor degradation and concept drift. In this paper, we present the evaluation of an adaptive machine learning method for predictive maintenance in EV battery systems.
We proposed a framework using the NASA Prognostics Center of Excellence (PCoE) lithium-ion battery dataset that uses an online adaptive learning and concept drift detection method to maintain model accuracy. A random forest regression model has been developed to predict battery capacity degradation based on extracted statistical and degradation-related features. The drift detection mechanism that is based on rolling mean absolute error (MAE) was used to identify performance degradation and trigger model retraining using a sliding window approach. The model result showed high predictive performance by achieving a high level of agreement between predicted and actual battery capacity degradation.
The statistical result shows a MAE of 0.061, a root mean square error (RMSE) of 0.11, and a coefficient of determination (R2) of 0.911. This shows its ability to capture nonlinear battery degradation trends accurately. The integration of a decision layer also enables maintenance to be prioritized by balancing system reliability and energy efficiency. This adaptive method addressed concept drift by capturing patterns and maintaining predictive accuracy through continuous model updates in EV battery systems.
S. A. Ajala, S. A. Animashaun, A. Olaleye· SPE Nigeria Annual Internati...· 0 citations
A structured methodology for ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models is proposed, offering valuable insights for developing efficient and scalable PdM solutions.
Sithik Shah· International Journal of App...· 0 citations
An integrated framework uniting enterprise asset management, predictive analytics, and digital analytics for maintenance prioritisation and decision support is developed by developing an integrated framework uniting data preparation, feature engineering, modelling, evaluation, reliability translation, and decision integration.
Adeyemi Adebukunola Ishekwene· Journal of Engineering Resea...· 0 citations
Timely asset maintenance remains a critical challenge in Industry 4.0 environments. Predictive Maintenance aims to anticipate failures and estimate Remaining Useful Life (RUL), enabling cost reduction and minimizing production downtime. However, real-world industrial scenarios are often characterized by noisy telemetry data, incomplete information about operating conditions, and weak degradation signals, which limit the effectiveness of conventional data-driven approaches. This paper proposes a methodology for RUL prediction under such challenging conditions, leveraging raw sensor telemetry without requiring detailed knowledge of machine operating characteristics. The approach introduces a novel Degradation Index, combined with a Health Index, to better represent degradation patterns. Additionally, signal preprocessing techniques, including Savitzky–Golay and Kalman filters, are applied to mitigate noise and improve data quality. The methodology integrates statistical analysis, similarity-based pattern extraction, and machine learning techniques, including Convolutional Neural Networks and Long Short-Term Memory models, for feature selection and prediction. Experiments conducted on real-world industrial datasets demonstrate that the proposed approach significantly improves prediction performance, achieving high accuracy and enabling failure anticipation up to five days in advance. The results highlight the importance of feature engineering and signal processing in PdM applications, showing that combining degradation modeling with deep learning yields robust, generalizable RUL predictions, even in noisy, partially observed environments.
Tiago Zonta, C. D. da Costa, F. Zeiser et al.· Scientific Reports· 0 citations
To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical. Traditional maintenance often proves insufficient under dynamic mission profiles. In this study, a deep learning-based predictive maintenance model capable of autonomously extracting features from multivariate sensor data was developed. Using the NASA C-MAPSS FD001 and FD004 datasets, data were converted into sequential blocks via 50- and 30-step sliding windows, respectively. The model's architectural superiority in autonomously extracting temporal degradation features was validated against RF, CNN-LSTM, and BiLSTM baselines. On FD001, it achieved an R-squared (R2) of 0.8901, a 13.28 RMSE, and a 320.34 NASA risk score, demonstrating generalizability on the multi-regime FD004 dataset with a 15.71 RMSE. The proposed maintenance protocol achieved a 0.9973 AUC at the critical 30-cycle threshold, ensuring high reliability. Additionally, a decision-support simulator has been developed to validate this protocol under aggressive combat flight profiles.
Fatih Ürgen, Dogay Altinel· 0 citations
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