Suppliers European Union Semiotic Labs
SUPPLIER MANAGED
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Semiotic Labs

Predictable Reliability
European Union
Leiden
2015
Private
< $10m
11 - 50
Open website

Passionate about solving the problem of unplanned downtime, Semiotic Labs uses advanced AI and Machine Learning technology to create smart condition monitoring products that increase productivity while saving clients’ time and money.

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SAM4 is a smart condition monitoring solution for AC motors and rotating equipment that installs inside the motor control cabinet and not on the asset in the field. SAM4 monitors equipment 24/7 and detects upcoming failures up to months in advance.

SAM4 is an advanced IoT solution composed of sensors, analytics and an online dashboard that offers actionable real-time insights into the health, performance and energy consumption of connected assets.

SAM4 is available as a scalable subscription service.

SAM4 IS AVAILABLE FOR:

  • AC motors up to 1,000 Ampères
  • Conveyors
  • Pumps
  • Compressors
  • Blowers & fans
  • Mixers
  • Gearboxes

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  • ArcelorMittal, Tata Steel, Thyssenkrupp
  • BASF, Nouryon, Evonik
  • Heineken, Henkel
  • SAPPI, CrownVanGelder
Read More
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Use Cases
Predictive Maintenance
Semiotic Labs’s Technology Stack maps Semiotic Labs’s participation in the IoT technology stack.
  • Application Layer
  • Functional Applications
  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service
  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
    Actuators
    Sensors
  • Devices Layer
  • Robots
    Drones
    Wearables
  • Supporting
    Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
None
Minor
Moderate
Strong
Number of Case Studies1
ArcelorMittal condition monitoring
ArcelorMittal condition monitoring
ArcelorMittal’s rotating assets often operate in harsh environments. A conveyor at the company’s hot strip mill in Ghent, Belgium moves plates of sizzling hot steel along the production process. In conditions like these, traditional proximity-based technologies like vibration and acoustic analysis fail: the sensors can’t handle the high temperatures.“In the steel industry, assets frequently operate in conditions that are not hospitable to sensitive sensor technologies,” says Andy Roegis, ArcelorMittal’s industrial digitalization manager for northern Europe. “The conveyor on our hot strip mill is a critical part of the production process, but it’s virtually impossible to use manual or vibration-based techniques to assess its condition.”
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