Research • Development • Innovation

SmartVortex Use-Cases

Four different use cases that prove the applicability of data stream technologies in industrial applications.

The SmartVortex project is focusing on data stream management technologies in combination with collaboration processes on streams that are generated either by sensor systems or from frequent outputs of computing programs.

Data Fusion

To calculate sensor data after the recording is one of the typical tasks in the "Big Data" scenarios. Data stream computing adds a new perspective that allow to validate data streams real-time without the need to store or record every element of the data stream. The data are as they are generated directly calculated and trend analyses established or thresholds examined. More than that these system allows also to merge sensor streams generated from different sensors and to combine those with computational algorithm. This produces data , which can be interpret directly without additional later-on following calculations. Especially for direct monitoring of processes this methods have strong advantages. The synonym for this technology is also called "Sensor Fusion".


Real-time data streams generated from sensors mounted on large Hydro Motor Systems are the base for the dimensioning of new motors and the analysis of the behavior of the hydraulic system. They are used to offer predictions on needed maintenance activities as well as to immediately react in the case of disturbances

Monitoring and Dimensioning



The design of new milling tools requires simulation models for the virtual tool design to get later-on the best performance in production. Sensor data streams verify on the prototypes the used virtual models and improve those to have best design results for future development activities. Additionally generate the calculated streams libraries for numerical control parameters to get optimal results in the milling process

Simulation Model Optimization



Wheel-loader get equipped with sensors and on-board data stream management systems connected over the CAN-bus. The On-board unit validates all generated data streams and connects to central monitoring centers in the case of male functions or upcoming maintenance needs. In collaborative stream visualization processes failures can be identified and best methods to eliminate the misbehavior can be discussed and brought a common decision and action.

Construction Machine Control



Structural optimization creates extreme high data volumes from each path of the FEM simulator output. Some optimization processes can last for days or weeks. Data steam management is used to analyze with trend calculations if the used parameter set will be able to generate the expected result. Prior spending calculation time in use less results the current calculation gets stopped and restarted with new parameter sets. This gives a massive time reduction for the entire process.

Structural Optimization


Focus of the use cases

The use cases have been selected to enable service oriented business models and to allow systems to respond real-time on alerts generated by the data stream management system.


Service Oriented Business Models

New service offers will expand the product portfolio of many companies. "Product as a Service" is getting wider attention in the industry.

What does it mean?

Instead of selling specific products as price list items the company offers a combination of parameters or characteristics which are standing for intangible assets, like:

  • Transport volume
  • Excavation volume
  • Torque
  • Constant pressure
  • Availability
  • ....

Together with those parameters usage conditions need to be specified to ensure that the product delivered has the identical usage paradigm as offered by the vendor and is designed to adequate to the conditions at the user's site. For some characteristics there might be seen a risk in operation be the vendors. Without surveillance of the products, maintenance strategies and monitoring capabilities those risks definitely are existing.

The Role of Data Stream Management

Data stream computing will help to reduce these risks by monitoring and validating critical elements of the product in real-time, initiating alerts and actions if deviations from the normal behavior are recognized and helping to gather experience about the behavior of the product.

New additional Business

Data stream computing can also help to establish additional service offers.

Obtained properties in machining or product usage represent a big value if they help to control automated systems. Parameter lists can conduce to generate new business opportunities and services in combination with the established product offers and contribute to the value preposition of the company's offers.