Effective Decision Support in Near-Real Time

Plant data historians are moving beyond their traditional role as tools to collect and archive data to better understand past plant performance, to becoming powerful tools that can be used to help improve real-time operations.

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With increased data throughput and higher data resolutions, historians have also become a foundation for plant asset management initiatives, thanks to new visibility and trending tools. Today's historians also support techniques, such as complex event processing, which can analyze multiple streams of plant data in real time to identify and diagnose emerging problems before they disrupt the production process in the plant, or negatively affect smart grid or other distributed assets.

Enhanced functionality

Recent product advances increase historian data throughput, solution scalability, compatibility and connectivity with plant systems and third-party solutions. They also provide powerful visualization and analytical tools. These allow users to access and leverage huge volumes of plant data in near real time. Historians can collect and display real-time data and events, giving users a more comprehensive view of what is happening in a plant or distributed assets. Historian suppliers have worked to offer improved data access and visibility tools with their solutions. Many offer Web-based, thin-client access to historians, and most offer access to historian data via mobile devices. Powerful trending and graphics tools allow users to generate custom reports and charts to visualize plant data.

With recent advances in computing technology, including 64-bit processing architectures, historians can collect and store large amounts of plant and process information; many can archive up to several exabytes of data. Many can simultaneously store and retrieve plant data, giving users an up-to-the-minute view of plant performance. Today's historians can handle hundreds of thousands of discrete events per second, so real-time plant data is available almost immediately for analysis. Modern computing power has enhanced historians to such a degree that rather than just being used to look back on plant performance, they can be used to predict and positively impact future performance.

The use of de facto standards and environments, such as OPC (an open connectivity standard) and Microsoft .Net, allows easier interfacing between systems and different historians. This helps users leverage existing historian data, even if they choose a new solution from a different vendor. OPC compatibility also enables easy access to and use of data from human-machine interface (HMI), distributed control systems (DCS), computerized maintenance management systems (CMMS) and other plant-level applications. Because  suppliers are also beginning to offer OPC-UA-compliant (OPC Unified Architecture) products, historian data is now also readily available to applications running on non-Microsoft platforms. In addition to plant-level equipment, historians also interface well with enterprise asset management (EAM), enterprise resource planning (ERP) and advanced optimization applications.

The ability to store, access and analyze plant data in near real time can help users identify any anomalies or troubling performance trends that could indicate a problem with plant production equipment. Historical data can be used to develop models or profiles that help users determine how a given asset should behave under normal conditions, and to set alarms or formulate maintenance strategies to balance production needs with asset viability, remotely and in real time.
 
Though in its infancy, complex event processing is another technology that can harness the capabilities of plant historians. Historians can be used to complement and augment complex event processing, a technology that can analyze multiple incoming streams of data in near real time. When viewed individually, these streams might mean little. But when viewed simultaneously and in context, they could help identify process or plant equipment problems using advanced data filtering and algorithms.

Allen Avery, aavery@arcweb.com, is an analyst at ARC Advisory Group Inc., in Dedham, Mass.
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