Using Data Historian

You can use Data Historian for a wide range of data logging and performance calculation tasks in industrial environments. By configuring Data Historian according to your specific needs, you can automate data collection, run performance calculations, and integrate with various systems for a comprehensive industrial monitoring solution.

Example Use Cases

  • Energy Management— Logging and calculating energy usage across multiple devices to optimize consumption.
  • Emissions Monitoring—Logging emission values and calculating hourly averages or exceptions for compliance purposes.
  • Production Line Monitoring—Tracking production outputs and generating real-time performance metrics.

To use Data Historian:

  1. Install and configure Data Historian.
    • Install Data Historian on your server and collector machines. Make sure your environment meets the system requirements (64-bit architecture, .NET Framework, and required SQL Server).
    • Configure the logging server by setting up the Data Historian logging server, which will store your historical data. You can also configure remote collectors to optimize data collection from distributed systems.
  2. Create data collection groups.
    • Open Workbench to launch Data Historian in Workbench, by navigating to MyProject > Historical Data > Data Historian, which is the centralized interface for managing data collection.
    • Add tags by creating data collection groups and tags for the parameters that you want to monitor (for example, temperature, pressure, and flow rate). Each tag represents a sensor or a data source.
    • Set aggregation to analyze data over time, such as calculating averages and maximum/minimum values. You can configure this by creating aggregate groups for different time periods, like hourly or daily.
  3. Use performance calculations.
    • Data Historian allows you to process data in real-time or post-process historical data to generate calculated values like:
      • Maximum, minimum, and averages over specified periods.
      • Custom performance calculations for tasks like monitoring emissions, calculating production metrics, or tracking energy consumption.
    • You can set up triggers to automate the calculations based on time intervals or data changes.
  4. Set up data redundancy (optional).
    • For mission-critical environments, configure redundancy to ensure continuous data collection and prevent data loss in case of system failures.
    • Redundancy can be applied at both the collector and logger levels.
  5. Access and use data.
    • Historical data access and visualization is available through OPC DA, OPC UA, HDA as well as dedicated GENESIS client applications like Trend Viewer.
    • View historical data as live data, which is useful for analyzing past performance as if it were happening in real-time. This can be integrated into SCADA systems for visual representation.
  6. (Optional) Export data.

    If you need to store or analyze data externally, you can configure the Data Exporter to export data to SQL databases, Azure storage, Apache Kafka, or comma-separated value (CSV) files.

  7. (Optional) Data Historian Importer
    • Use Data Historian Importer to ingest historical data from third-party historians or other file types containing historical data that may not be able to be collected automatically.
    • Imported historical data can reside in .csv or .txt files.
    • You can develop your own custom collectors and plugins using the available .NET C# toolkit.