Integrating Time-Series Data with Geospatial Systems

In addition to using time-series monitoring of critical infrastructure assets, it has recently become possible to monitor the performance of wide-area critical assets and facilities in real time by integrating real-time operations data into geospatial analytics and displays. Esri, the worldwide leader in geographic information systems, developed a geospatial platform called ArcGIS, which provides a sophisticated, enterprise-scale geospatial information system (GIS) platform, which allows users to create very' intelligent maps containing independent layers of data. The data may be static or real-time data, depending on context and the need to update continuously.

Although this may not be critical in a refinery, this marriage of realtime and spatial technologies provides significant benefits in industries where assets are widely dispersed over a large geographic area. Some examples are

  • • Pipelines,
  • • The electricity grid and its associated substations,
  • • Upstream oil and gas fields,
  • • Water networks,
  • • Wind turbine farms, and
  • • Campus environments to monitor energy consumption by individual buildings.

The benefits also extend to monitoring fleets of mobile assets, such as mining trucks, where companies can get real-time visibility into where each truck is currently located and the health of the truck (via time- series data), and use spatial analytics to calculate optimal routes based on dynamic conditions.

Some real-time data infrastructures, such as OSIsoft's PI System, have developed standard off-the-shelf integration methods that help operating companies manage the integration of time-series and geospatial data.

Customers can visualize real-time energy consumption and related cost data by using intelligent maps and dashboards to see the status and condition of building environments on a regional, national, or global basis. By integrating real-time plant or facilities data in geospatial displays and analytics, enterprises can significantly increase their energy usage awareness, recognize data patterns and trends in real time, and utilize powerful spatial analysis tools to explain why issues are occurring.

The EIDI (PI System) AF asset data model, discussed in Chapter 3, can be exposed to the GIS for asset-relative, intelligent map visualization. In addition, asset data from the EIDI system can be integrated with the GIS to utilize spatial (non-time based) analytics. Figure 10.9 represents an Esri ArcGIS intelligent map with KPIs, real-time PI System data, historical trending, and spatial analytics (Lopez 2015).

For more information on integrating real-time data infrastructure with a GIS (Esri ArcGIS), see www.osisoft.com/GIsintegrations.

FIGURE 10.9

PI Asset Framework integration with Esri ArcGIS.

 
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