Asset health and reliability

AI-driven analytics and contextualized data are integrated to power a new level of insights, for significantly enhanced threat detection and issue resolution that prevents downtime and production losses.

Assess and predict the health of physical assets, with workflows and solutions that combine domain expertise and digital capabilities. These highly scalable, modular workflow solutions—for facility uptime management and equipment twin analysis models—overcome disruption, downtime, associated costs, elevated risks to personnel safety, and potential fugitive emissions.

Solutions are driven by a continuous stream of data, contextualized across equipment, facilities, and wider production operations—taking into account the ‘big picture’ with integrated workflows, in sharp contrast to traditional approaches.

Equipment twins incorporate data-driven AI/machine learning (ML) techniques, physics-based analysis, and other simulation methods to detect anomalies and predict faults and failures, days, or even weeks, ahead of time.

Realize value from:

• End-to-end full facility monitoring solutions.
• Fully contextualized asset information.
• Enablement and decision-support for threat management, investigation, and mitigation.
• Integrated results and insights from digital twins.

A range of subject matter expert (SME)-led process optimization and uptime assurance services integrate with digitally enabled equipment, in collaboration with OEM experts, and maintenance to enhance asset life cycle management.

Results include:

• 88% compressor train failures predicted in advance.
• 15 days unplanned oil treater downtime avoided.