The broad availability of affordable computing power, massive data storage and low-cost sensors started a revolution in condition-based maintenance. It is now possible to deploy complex workflows dedicated to collect asset data, detect asset anomalies, and estimate remaining useful life to optimize asset maintenance. Analytics plays a leading role in all such workflows, leveraging the tremendous amounts of data generated by IoT sensors, maintenance systems and operating systems.

Oil & Gas services companies have taken their first steps in implementing condition-based maintenance schemes across their product lines. Failures in equipment during field operations heavily contribute to maintenance costs and non-productive time. Service companies quickly realized of the huge benefits health management can have for their bottom lines as well as for their reputations with customers.

This presentation will describe our typical Analytics project phases: Data Ingestion, Exploration & Transformation, Algorithm Development, and Deployment. Different applications will be reported. The first one illustrating an offline health analysis and maintenance prescription for a donwnhole logging-while-drilling (LWD) tool. The second application demonstrating an approach for automated condition monitoring of subsea blow-out preventers (BOPs).