Failure prediction of oil and gas equipment using machine learning

Abstract

The article addresses the problem of improving the reliability of technological units at oil and gas enterprises by transitioning from a regulatory and reactive maintenance model to predictive failure management based on an ensemble of machine learning methods. The aim of the study is to develop and verify a comprehensive methodology that ensures early detection of pre-failure conditions and aligns forecast horizons with maintenance and repair schedules. The empirical base includes five-year multidimensional time series (2018–2022) for centrifugal pumps, reciprocating compressors, and separators ( свыше 15 TB, 40–70 parameters per unit), obtained from process control and vibration diagnostics systems. Preprocessing covers noise filtering via Kalman filter and wavelet transforms, interpolation of missing values, standardization, and feature engineering (moving statistics and FFT for vibration). Random Forest, gradient boosting (XGBoost), and LSTM were compared using pre-failure window labeling, a 70/15/15 split, and Precision, Recall, F1, and AUC ROC metrics; SMOTE was applied to handle imbalance. The final architecture was implemented as stacking with a logistic meta-model. It was shown that boosting is optimal for pumps (highest Precision and F1), while for compressors and separators the LSTM better captures long-term temporal patterns. The ensemble outperformed baseline models (F1 = 0.9345; AUC ROC = 0.971) and provided forecast lead times of 48–150 hours. Feature-importance analysis aligns with degradation physics: vibration RMS and bearing temperature dominate for pumps, pressure pulsations and discharge temperature for compressors, and pressure differentials and rate of level change for separators. The discussion highlights the need for differentiated algorithm selection based on failure mechanisms, portfolio integration of models, and a built-in trade-off between Precision and Recall to minimize false alarms while avoiding missed failures. Practical implementation envisions integration with process control and enterprise asset management systems to automate decision-making. Limitations include data heterogeneity and the rarity of events; future directions involve transformers and graph networks for complex dependencies, XAI for interpretability, and integration of text logs (NLP) for contextualized forecasting.