Monitoring & Surveillance

Production Surveillance increases asset awareness and predictability by analyzing asset data, and combining it with available knowledge about the asset to provide insights about the asset’s performance.

In the digital asset, Exception Based Surveillance leads to advanced diagnosis and recommends optimal corrective action which can be easily translated into field action through the automated workflows.

Production surveillance faces multiple challenges

  • The asset has too much data to look at for too few people
  • Issues are spotted reactively rather than proactively, leading to unscheduled downtime
  • Surveillance engineers are overwhelmed with alerts, or they don’t know what to do with them
  • The surveillance system cannot interact with enough different disciplines at any one time
  • Surveillance is limited to only what can be measured directly

The benefits of automated surveillance

  • Monitor more key performance indicators of the asset with fewer people
  • Shorten intervention times
  • Lower unscheduled deferred production
  • Respect equipment operating limits more rigorously
  • Lower downtime and lengthen equipment lives and asset lifetimes

What defines IPCOS Digital Asset Surveillance?

IPCOS Digital Asset Surveillance is self-service, on-demand surveillance with completely automated routine analysis. Engineers work in a supervisory mode and exceptions are managed collaboratively.

IPCOS Digital Asset Surveillance is context aware and uses analytics to enable effective interpretation of measured data in combination with physics and data models. This process and data model integration provides the holistic context of the asset, to model and predict the asset behavior and response to interventions.

IPCOS Digital Asset Surveillance translates system recommendations to action. It makes it easy to create action plans, work orders and follow-up procedures through automated and guided workflows.

Best practices

  • Placing sufficient focus on data quality and reliability
  • Managing alarms, and limiting the number of alarms raised
  • Making surveillance techniques proactive to generate early warnings rather than reactive alarms
  • Managing limits centrally to ensure clear accountability for maintaining and modifying limits
  • Making exceptions actionable through review, acknowledgement and action
  • Integrating surveillance models with integrated modeling packages or solutions