Title : Predictive maintenance in oil and gas facilities using nonlinear signal analysis and machine learning
Abstract:
Predictive maintenance has become a critical challenge in modern oil and gas facilities due to aging infrastructure and harsh operating conditions. This study presents a hybrid methodology combining nonlinear signal analysis and machine learning for predictive maintenance of critical equipment such as pumps, compressors, and pipelines. Key features extracted from operational signals include entropy measures, frequency-domain characteristics, and nonlinear dynamical indicators. These features are then used to train a supervised learning model for early anomaly detection and failure prediction. Experimental results show that the proposed framework outperforms classical statistical methods in identifying incipient faults, reducing false alarms, and improving system reliability. This approach offers a scalable and efficient solution for enhancing asset integrity management in oil and gas operations.
