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Data Historian Accelerator
Capture real-time telemetry from data historians like OPC UA and OSI PI with TIBCO's Data Historian Accelerator. Navigate object hierarchies with an easy to use interface to setup subscriptions on data points of interest. Map these data points into logical data sets and then pass this to custom-implemented rule modules using decision tables and data science models to gather insights. Stream this data into Live Datamart for real-time visualization of system state and take action when anomalous behaviour is detected.
TIBCO Spotfire® TIBCO® Streaming
TIBCO Spotfire Analyst
TIBCO Spotfire Server
TIBCO Artifact Management Server
TIBCO Component Exchange License
The Data Historian Accelerator contains components that capture historian data from a number of sources, such as OSI Pi and OPC, via APIs.
The list of Supported Versions represents the TIBCO product versions that were used to build the currently released version of this accelerator. We expect newer versions of the TIBCO products will also work. Please see the wiki page for the accelerator for possible further details around product versions.
Accelerators are provided as fast start templates and design pattern examples and are supported as delivered. Please join the Community to discuss the use and implementation of the Data Historian Accelerator.
OSI Pi is a monster in the industry for IoT and SCADA systems. We're looking into doing some really interesting things with it in 2021, and this Accelerator is not only a great template to get you started but also a Petrie dish for innovation. Let us know what you think!
Data Historian Accelerator
The Data Historian Accelerator captures real-time telemetry from data historians like OPC UA and OSI PI. A custom HTML5 web interface provides the user the ability to visualize the object hierarchy of the historian, and create subscriptions on the nodes or tags of interest. The accelerator receives this data in real-time and assembles the points into logical data sets that can then be passed to business rule modules that implement decision tables or data science models. The data and model output are streamed into Live Datamart for visualization in Spotfire.
The TIBCO Intelligent Equipment Accelerator is a similar offering, but is a generic platform for any data provider, whereas the Data Historian Accelerator focuses specifically on systems like OPC UA and OSI PI.
(Since the last release of Data Historian Accelerator 1.0.0)
March 24, 2021, release of Data Historian Accelerator 1.1.0
- Upgraded to latest Spotfire 11.2.0
- Enhanced OSI PI subscription and data discovery
- Added new trigger reload button for data sources
- Added new setting for database name for OSI PI data sources
- Minor bugfixes and enhancements
Data Historians are applications that retrieve production and process data from manufacturing and other process-oriented systems. They store data in an efficient database reducing the requirement for large amoutns of disk space. They also provide quick access to the data through API-based queries.
Historians are a mature technology. OSI PI is over 40 years old, for example. They have large existing install-bases and are well-integrated with manufacturing and process-based technologies through DCS and PLC control systems. However they do not contain native advanced analytics capabilities and the ability to execute machine learning models.
The Event Manager implementation handles the connection to the historian systems and full processing of the data. The following concepts and terms apply to this component.
Data Source is an implementation of a connection to a historian system. It includes some standard web services that allow the user to browse the hierarchy on the historian and setup Subscriptions to data points of interest. The Accelerator refers to these data points as Nodes but this will map on to different concepts depending on the historian being connected to. In OPC UA these are also referred to as Nodes but in OSI PI these refer to Tags.
Once a Subscription is setup to one or more Nodes, we can assemble these into a logical set of data points called a Feature Set, with each data point mapping to a single Feature. Each Feature Set may have one to many Features, which typically will all come from the same Subscription or Data Source, but may span different Subscriptions and Data Sources if necessary.
A Feature Set can then be directed to call one to many Indicators. An Indicator is an implementation of a business rule that requires certain data to operator, which is modelled as the Feature Set. Indicators are called in an pre-defined order, with the output of earlier Indicators passed into the subsequent Indicators, along with all Features from the initial Feature Set, unless these have updated by preceeding Indicators.
An Indicator can implement any kind of arbitrary business logic as required. The Accelerator provides examples of Indicators that compute the mean of all a set of Features using TERRTM and Python. There is also an example that computes a cluster using PMML. Indicators may implement any other standard EventFlow logic, including Decision Tables. They may output a Feature Set of Features which can include new Feature values or updates to Features passed in to the Indicator.
All raw data from a Data Source are passed to Data Sinks for storage. The Accelerator implements a single Data Sink for TIBCO Live Datamart, but this could be extended to other sinks such as CSV files. All Features in a Feature Set are also sent to the same Data Sinks for storage. This includes both original Feature Sets as well as new and updated Features generated by Indicators.
Benefits and Business Value
By integrating TIBCO Data Science, TIBCO Spotfire® and TIBCO Streaming with these Data Historians, process data can be captured and and analysed to detect patterns. Models can be developed using languages like R or Python, or developed using more advanced tools like StatisticaTM which can then be deployed to a running TIBCO Streaming engine for real-time model execution. These models could be used to detect anomalies, detect production quality issues, or do predictive or condition-based maintenance.
The Accelerator shows how to integrate Spotfire, Statistica, and TIBCO Streaming to capture historian data and execute against some simple models.
Two datasets are provided:
- Electric Submersible Pump (ESP) telemetry for intake pressure and current draw
- Power plant telemetry for a gas-fired turbine plant which generates electricity
Simple model implementations in R and Python are provided to show how to integrate with inbound historian data. These models simply compute a mean of all provided features.
A more sophisticated K-Means Clustering model is provided using PMML that can be used to detect anomalies in the power plant telemetry data.
TIBCO software products and versions used
|TIBCO Spotfire Analyst||11.2.0|
|TIBCO Spotfire Server||11.2.0|
|TIBCO Streaming Artifact Management Server||1.6.1|