From the April issue of Pumps and Systems Magazine. Explore the innovative solutions addressing the significant issue of unplanned downtime in the oil and gas sector. This article delves into a hybrid approach that combines physics-based models and machine learning algorithms to improve the accuracy of predictions of reciprocating compressor valve faults, enabling earlier detection and reducing unscheduled shutdowns. You’ll also learn how virtual sensors and physics-informed machine learning eliminate the need for crank angle sensors, reducing retrofit costs.

Learn from a compelling case study highlighting the successful implementation of this cutting-edge solution in an oil processing plant in the Permian Basin, demonstrating its effectiveness in predicting valve leakage. Gain valuable insights from this article to enhance your understanding of equipment maintenance and strategies to minimize downtime.