Rotating equipment communicates its condition through the electrical current drawn by its motor. Motor Current Signature Analysis (MCSA) converts that signal into diagnostic evidence—often while the machine remains online and without installing sensors directly on the equipment.
One signal, multiple failure indicators
Changes in the current spectrum can reveal developing problems associated with rotor bars, air-gap eccentricity, load variation, misalignment, bearing-related effects, and other electrical or mechanical anomalies. Trending these signatures helps distinguish a persistent defect from a temporary operating disturbance.
Stronger when evidence is integrated
MCSA is most valuable when combined with operating context, maintenance history, vibration findings, and engineering assessment. Logaritm AI uses this integrated view to move from anomaly detection to a practical recommendation: continue monitoring, inspect, adjust operation, or intervene.
The result is earlier warning, better maintenance prioritization, and fewer unnecessary shutdowns for critical motors and driven equipment.
