SAM4 - Smart Condition Monitoring for AC motors and rotating equipment

Smart Condition Monitoring for Rotating Assets
Published by Samotics


  • Highlights


Explore Intelligence

  • SAM4 monitors assets at scale, by automating analysis based on concepts borrowed from Motor Current Signature Analysis.
  • Algorithms allow SAM4 to process large amounts of data and provides accurate analysis across a wide variety of motors, assets and circumstances without editing a single line of code.
  • Classification

  • Classification algorithms detect patterns in data that are associated with specific failure mechanisms.
  • The system identifies the patterns that emerge, and can identify a soft foot from a clogged pump. We call these patterns "fingerprints of failure".
  • Misalignment can be identified by an increase in energy at the rotational frequency modulated on the main frequency of the current.
  • Execution is not straightforward: A host of variables influence the shape of these patterns. SAM4 takes all these variables into account, including:

  • The make and type of motor
  • The load and speed of the motor at the time of measurement
  • The driven equipment
  • Frequencies introduced by the inverter
  • Anomaly Detection

  • Anomaly detection algorithms detect irregularities in patterns.
  • Higher anomaly scores means that the observed patterns deviate from the expected, healthy patterns and represents a heightened risk of failure, triggering targeted inspections.
  • Low anomaly scores means that the asset is behaving as expected and represents a very low risk of failure. This allows maintenance technicians to prioritise maintenance tasks.
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