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Intelligent Equipment Accelerator

Capture and analyze sensor data in real-time from your IoT devices with TIBCO's Intelligent Equipment Accelerator. Integrate through industry-standard protocols like OPC UA, OSI PI, MQTT, and Web Services, or build your own. Apply custom validations, cleansing policies, rules, and feature statistics to data feeds to identify trends and gain insight.

Compatible Products

TIBCO Spotfire® TIBCO® Streaming


TIBCO Software

Compatible Versions

Software Minimum Version
TIBCO Spotfire Analyst 11.8.0
TIBCO Spotfire Server 11.8.1
TIBCO Streaming 10.6.2
TIBCO Streaming Artifact Management Server 1.6.2


TIBCO Component Exchange License


The Intelligent Equipment Accelerator contains components to monitor of sensor data from telemetry-enabled equipment and Internet of Things (IoT) devices. User-defined data sources can be created using industry-standard protocols like OSI PI and OPC UA, or general messaging middleware like JMS, FTL, and MQTT. Data is validated and cleansed with user-configured modules. Customizable business rules and featurization modules can be built and defined in Streaming EventFlow and then attached to data feeds using configuration to capture real-time insights in the data stream. Both Spotfire and web dashboards are provided to visualize these insights and give the opportunity to take action.


The source distribution contains a Quick Start guide in MD format. For hybrid cloud deployment, view the Intelligent Equipment Getting Started in the Cloud guide on TIBCO Community for setup instructions.


Support Details

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 Intelligent Equipment Accelerator.

License Details

Component Exchange License


Release 4.1.0

Published: February 2022

Updated to latest version of Spotfire and TIBCO Streaming, new configuration pages and new hybrid cloud architecture deployment model.

This Accelerator is distributed with 2 zip files. The Distribution ZIP (dist) is only for deployment into hybrid cloud architecture. The Source ZIP (src) is for local or virtual machine deployment.

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Intelligent Equipment Accelerator


The Intelligent Equipment Accelerator provides a reference architecture and code assets for building telemetry monitoring solutions inside of equipment hierarchies. It is primarily configuration-driven which allows a flexible object hierarchy based on the generic concept of Entities. Attached to these Entities are Devices which represent data producing sensors. The platform illustrates how capturing sensor telemetry can be used to gain business insights.

This video demonstrates the Accelerator in action:


What's New

(since the last release of Intelligent Equipment Accelerator 4.0.0)

April 12, 2022, release of Intelligent Equipment Accelerator 4.1.0

  • Upgraded to latest Streaming 10.6.2, EBX 6.0.5, Spotfire 11.8.0
  • Added feature set, metadata, and instance configuration user interface
  • Added new hybrid cloud deployment model for 100% cloud experience

Business Scenario

Most modern equipment are instrumented in some way with a variety of telemetry captured from sensors, from cars to electronics to lightbulbs. Gathering this data and making sense of it all is a key problem for owners of this equipment. Once data is captured either on edge devices or within a core infrastructure, it then becomes a challenge to detect patterns and meaningful behaviours from the noise. Through the use of rule-based systems and data science models, actionable insights can be gleaned. That allows the ability to take action in developing situations, or just capture the data to refine models for future improvements to the system.


The Intelligent Equipment Accelerator has a generic data model that is configuration driven. At the top level there are two main concepts:

Devices -- are anything that produce a stream of data. Also known as sensors. Typically produce data triplets at high frequency, consisting of a unique identifier, a timestamp, and a data value. Devices are attached to a single Entity, but an Entity can have multiple Devices.

Entities -- are anything else. This can be factories, production lines, equipment, aircraft, buses, ovens, drilling rigs... anything. Organized into hierarchies, one Entity may have a single parent, but multiple children.

To help with configuration, the Accelerator also separates configuration into Templates and Instances.

Instances -- are physical examples of Devices or Entities, equivalent in object-oriented programming to an Object Instance. They are linked to a single Template, have a physical location, and a unique identifier like a serial number.

Templates -- definition of common properties for all Instances of a given Template, equivalent in object-oriented programming to a Class. May also be known as a type. Will not have a physical location or a unique identifier like a serial number (but could be a unique model number).

Since Devices often send only single data points at a time, it is often useful to aggregate these together into virtual rows of data for processing.

Features-- are linked to a single Device Instance or to a Device Template associated with an Entity Template. Represents a single value in a virtual row.

Feature Sets-- are logical groupings of Features into a single virtual row. This virtual row can then be passed to rules and data science models to evaluate multi-variate conditions and states.

This configuration looks like this:

In addition, users can configure Modules which link to physical EventFlow application modules implementing specific business rules or interfaces. These may be implemented as Validation Modules, Cleansing Modules, or Rule Modules. These modules are then linked to Devices, Device Templates, and Feature Sets so they are called during the processing of data from these data sources.

The Accelerator captures data feeds from external systems as reports.

Alert -- reports from external systems of alert conditions.

Reading -- device reading reports consisting of a triplet of unique identifier, reading date and time, and the value.

Status -- a condition status for a given Device or Entity. For example, a production line or pump operating status.

Part -- a part produced report used as part of operational metrics.

Position -- a physical location for a given Entity that may change over time.

After processing the inbound reports, the Accelerator produces external actions.

Alerts -- similar to Alert reports, indicates an alert condition on a Device or Entity.

Status -- similar to Status reports, indicates the status of a given Device or Entity has changed.

Readings -- certain rules may produce additional Feature values as part of the rule execution. For example, an autoencoder rule may generate a cluster number and a reconstruction error. These Feature values are produced as new Readings.

The dynamic data model looks like this:

Benefits and Business Value

Most modern equipment today are instrumented with some sort of sensor. We can use the streaming data from these sensors, combined with context information from various systems to gain a complete real-time view of all operations in order to rapidly resolve current issues and intervene to address preventable problems before they occur.

Technical Scenario

The Accelerator provides a generic data model for building entity and device hierarchies with a configuration interface. The included demos capture sensor data from a number of devices installed on equipment in their respective environments. These demo scenarios are:

  1. Production oilfield with a series of wells using electric submersible pumps (ESP). The Accelerator captures telemetry and attempts to identify a failure pattern and alert when this looks likely.
  2. Heavy equipment monitoring engine signals for preventative maintenance
  3. Power plant where the overall state of the generation lines in the plant are computed using both an R model using a K-means clustering algorithm, and an H2O model using an Autoencoder algorithm.
  4. Servers showing monitoring of an IT infrastructure hierarchy showing infrastructure, platform, and service level monitoring.
  5. Widgets showing operational analytics monitoring the production of parts from various factories and production lines

The Accelerator is based around a single TIBCO Streaming engine called the Event Manager. This engine receives a defined set of reports from multiple sources either through directly enqueued stream data or through a JMS receiver. In the demo the Simulator connects to the Event Manager through the internal messaging bus. In a real implementation the integration of data sources will always be a project and will likely require development of adapters and ingress EventFlow to transform the data into the Accelerator canonical formats.

As device readings flow through the Event Manager, they are subjected to several analyses, validation to ensure the data is correct, cleansing, business rules, summarizing, and statistics calculation. The results of these are pushed through to Live Datamart as appropriate, and a fully custom HTML5 application can be used to view the contents, as well as Spotfire.


Software Minimum Version
TIBCO Spotfire Analyst 11.8.0
TIBCO Spotfire Server 11.8.1
TIBCO Streaming 10.6.2
TIBCO Streaming Artifact Management Server 1.6.2


Documentation Quick Start Guide is available by downloading the full Accelerator distribution package from here . To get started with the hybrid cloud architecture, view the Intelligent Equipment Getting Started in the Cloud guide on TIBCO Community, and the related distribution package can be downloaded from here. The license file for this component is here.

Back to main Accelerator page

View the Wiki Page