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WHY SYSTEM MODELS?

  • Do you know early on if the requirements can be reached?
  • Are you sure that the optimization potential justifies the effort?
  • What are the key performance parameters and what drives them?
  • Can I derive degradation states in my machines?
  • Do you want to go beyond counting operation hours in Industry 4.0?

TRANSIENT LOW-ORDER MODELS

First principle modelling and dimensional analysis is the perfect tool to validate concepts and quantify the optimization potential. As the complexity of the models is kept simple at an initial phase but grows with the task, the approach is very efficient.

Transient physical models are the basis of a model-based approach in digital monitoring and diagnosis.  Unlike a purely data-driven approach, a model-based setup works reliably and generate value even with very few data and limited observation time.

COMPUTATIONAL FLUID DYNAMICS  (CFD)

In fluid- and thermodynamics it can be verry difficult to understand the complex phenomena or to measure the required quantity directly. In such CFD is a very powerful tool to analyze and optimize the design.

In the last years, simulation tools and software on demand platforms have gone a long way to make CFD more accessible to the non-specialized engineer.

This means our focus has shifted from setting up and running the simulation to help correctly formulating the problem, reducing the complexity of the system to be simulated and correctly setting the boundary conditions and especially to make most of the results obtained.

SENSOR SELECTION AND SIGNAL PROCESSING

As a simulation, a measurement tells only part of the truth. Measurements can be very costly and should only be considered after careful formulation of the problem statement selection of sensors and data acquisition.

Digital monitoring and diagnosis applications require a thorough understanding of the links between the parameters to be observed and the signal respective sensor signals. The selection of sensors influences deployment feasibility greatly. A bad choice can lead to non-detection or false alarms.

We help you to draw the right conclusions from the measurement signals.

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