Predictive Maintenance of Oil and Gas Equipment using Recurrent Neural Network

Abstract

Oil and gas industry projects involving equipment acquisition and installation are usually capital intensive. The recent crude oil price fall has tightened the expenditure and therefore reinforced the importance of effective maintenance management across the oil and gas industry. Rotating mechanical equipment such as induction motor, compressors and pumps, are essential elements in industrial processes. Effective maintenance of these equipment is crucial to avoid several damage and downtime for repair. Predictive maintenance has attracted huge attention in this industry driven by sensors and data acquisition. This paper focuses on developing machine learning algorithm based on recurrent neural network (RNN) using long short-term memory (LSTM) to carry out predictive maintenance of Air booster compressor (ABC) motor. The resulting experiment demonstrates the performance of RNN-LSTM algorithm implemented for fault prognosis model of rotating equipment predictive maintenance. The application of these algorithms could mitigate risk and reduce cost in the oil and gas operation.