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Internet of Things

Data that processes and machines collect on their own functions and operation and from their surroundings can be collected and enriched with analysis, prediction and optimising models to grow and support business.

  • Predictive maintenance: The wear of maintenance-critical parts under different operating scenarios is predicted on the basis of sensor data. Answers the following question: when should maintenance be carried out in order to maximise production volume/runtime?
  • Path analysis: The model identifies which series of events trigger failures, stoppages or alerts.
  • Process control: Modelling provides data on how the different settings of one part of a machine affect other parts, e.g. how the wear of part X can be prevented by adjusting other parts of the same equipment.
  • Real-time process monitoring: Follows the parameters of process-critical components and proactively issues alerts when defined limit values are approached.
  • Modelling of energy and raw-material consumption: Optimises the amount of energy and raw materials needed to reach targeted production volumes.
  • Benchmark modelling: Used to identify differences between manufacturers, and the combinations of components and operating parameters that are optimal for production.
  • KPI view: Combines data from processes and machines into a KPI view to assist decision-making.
  • Visualisation: A tool for describing IoT data trends and interconnections.

http://blog.houston-analytics.com/blog/basis-of-iot-data-analytics-prediction-and-optimization

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Contact

Juha Raunama
Director, Customer Solutions
Houston Analytics Oy
tel. +358 400 845 908
juha.raunama@houston-analytics.com

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Houston Analytics Ltd
Verkkosaarenkatu 5, 00580 Helsinki
c/o Pedab Denmark 2. sal, Vibeholms Allé 16, 2605 Brøndby
Business ID: 2574184-1

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