AiVibeQuality, Reliability & Maintenance
Motor Current Signature Analysis (MCSA)
Motor current signature analysis is a condition monitoring technique that analyses the frequency spectrum of an electric motor's supply current to detect electrical and mechanical faults in the motor and, in some cases, in the driven equipment.
An induction motor's stator current contains components at the supply frequency and at additional frequencies created by faults that modulate the air gap or the magnetic field. A current clamp or current transformer on one or more phases captures the signal, often at the motor control centre, and spectrum analysis reveals these components. A classic example is broken rotor bars, which produce sidebands around the supply frequency at plus and minus twice the slip frequency. Air gap eccentricity, some bearing faults and load-related problems such as misalignment or belt and gear issues can also appear as characteristic current components.
MCSA is attractive because measurements can be taken remotely from the motor control centre without access to the motor itself, which suits motors that are submerged, hazardous to approach or hard to reach, such as borehole pumps and fans in ducts. It is used in water utilities, process plants, mining, power generation and manufacturing, and is often combined with voltage measurement in broader electrical signature analysis. Drives and motor protection relays increasingly provide current data for such analysis.
Interpretation needs accurate nameplate data, such as the number of poles, rotor bars and rated speed, and depends on load, since some faults are visible only at sufficient load. Supply harmonics and drive switching can complicate spectra on inverter-fed motors. Measurements on energised electrical equipment must follow electrical safety procedures and be performed by qualified personnel.
Key points
- Analyses the motor supply current spectrum to detect faults.
- Broken rotor bars give sidebands at twice slip frequency around the supply frequency.
- Measurements can be taken at the motor control centre, away from the motor.
- Results depend on load and accurate motor data.
Where AiVibe comes in
AiAmbA AI Factory use cases include predictive maintenance and visual inspection on textile, automotive and electronics lines. AiAmbA IoT normalises industrial signals from protocols such as OPC UA and MQTT, with driver availability confirmed per installation.
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