Enhancing predictive maintenance strategies for oil and gas equipment through ensemble learning modeling
Mei Wang, Xuesong Su, Huifang Song, Yifei Wang, Xin Yang
Abstract
In the field of oil and gas equipment management, frequent maintenance is conducted, resulting in unnecessary costs. Relying solely on a single artificial intelligence model often leads to low predictive accuracy and inadequate robustness because of the poor data quality. Therefore, a method based on ensemble learning modeling is proposed to accurately assess the health status of industrial equipment and predict its remaining useful life. By conducting big data analysis on the operational history data of all oil and gas equipment, various fault instances are extracted and grouped accordingly. The integration of meta-learning convolutional shrinkage neural networks (ML-CSNN), domain expert rules, and support vector machine (SVM) models forms a hybrid model aimed at constructing a robust classification model. The effectiveness of the proposed method is validated using operational data from shengli oilfield production wells in China. The proposed method achieves 0.98 in accuracy, 0.93 in precision, 0.94 in recall, and 0.93 in F1 score, which is an improvement of 9–25% compared to the popular integrated learning models, such as GBDT and XGBoost. By designing the ablation study, results demonstrate the method’s ability to accurately predict potential faults of oil and gas equipment, thereby facilitating the enhancement of predictive maintenance strategies.