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SAM4 - Smart Condition Monitoring for AC motors and rotating equipment
Smart Condition Monitoring for Rotating Assets
Published by
Samotics
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SAM4 - Smart Condition Monitoring for AC motors and rotating equipment
Published by
Samotics
$1500
Features
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.
Highlights
Explore Intelligence
Classification
Execution is not straightforward: A host of variables influence the shape of these patterns. SAM4 takes all these variables into account, including:
Anomaly Detection