Rotor defects, cavitation, bearing wear and coupling problems change the current your pump motor draws. Artesis reads those changes at the motor control cabinet, so nothing has to be fitted to the pump.
Cooling water, condensate, process and fire water pumps run around the clock, often in pits, basements, hazardous areas or behind guards. Route-based vibration rounds catch a snapshot every few weeks; a developing fault can grow in between.
Because the pump and motor are mechanically coupled, what happens in the impeller, bearings and coupling shows up as small, characteristic changes in motor current. That is the signal Artesis follows continuously.
e-MCM clips onto the existing current and voltage signals. No work on the pump and no cabling out in the field; installation is planned electrical work inside the cabinet.
Electrical Signature Analysis and machine learning build a baseline for the specific motor–pump set under its real operating conditions.
Deviations are classified by fault domain — electrical, mechanical, hydraulic — and explained in plain language with maintenance guidance.

Top: Pump 13, with a rotor-related pattern. Bottom: identical Pump 12, without it. Select to open full size.
Repeated test, consistent result
A dedicated rotor evaluation test was run three times on Pump 13; the rotor-bar indicator stayed at alarm level each time.
The findingOnline monitoring flagged a spectral pattern on Pump 13 associated with rotor bar defects.
The cross-checkPump 12 is identical in every respect and runs the same duty. Its spectrum showed no such pattern, ruling out supply or process effects.
The confirmationAn offline rotor evaluation test indicated cracked or defective rotor bars. The test was repeated three times with consistent results.
Source: Artesis Case Study 2024, slides 81–87. Customer name withheld.

The high-frequency spectrum showed the typical signature of cavitation. The customer’s vibration check on the pump side agreed.

Bearing-related components rose above the learned envelope and the spectrum also pointed to flow turbulence. Teardown found a worn bearing, broken coupling rubber and cavitation dents on the vane.

The automatic bearing analysis, based on the bearing model, matched the observed spectrum and pointed to an outer-race fault.
On a diesel fuel pump, spectrum trends were followed through several shaft adjustments until the alignment was confirmed as correct.
On two seawater pumps aboard an LNG carrier, monitoring pointed to rub, misalignment and impeller issues. Repair returned the pumps to normal efficiency.
Cases are individual outcomes from Artesis field deployments between 2005 and 2024. All visuals are from Artesis software. Customer names are withheld.
Evidence strength reflects how reliably a condition produces an observable electrical pattern in typical pump installations — not a guarantee of detection.
| Condition | What changes in the signal | Evidence | Field case |
|---|---|---|---|
| Motor · electrical | |||
| Broken or cracked rotor bars | Rotor-related pattern in the current spectrum | Strong | 11 kV condensate pump → |
| Stator winding and insulation | Internal electrical fault indicator and phase balance | Strong | — |
| Supply and connection issues | Voltage imbalance, harmonics, RMS deviation per phase | Strong | — |
| Drive train · mechanical | |||
| Bearing wear | Bearing-related pattern in the current spectrum | Good | Jockey pump, cooling pump |
| Misalignment and coupling | Coupling and alignment-related pattern | Good | Cooling pump, fuel pump |
| Unbalance and looseness | Unbalance and looseness-related patterns | Good | — |
| Pump · hydraulic and process | |||
| Cavitation | Characteristic hydraulic pattern in the spectrum | Good | 6.6 kV pump |
| Flow turbulence, impeller damage | Flow-related pattern in the spectrum | Good | Cooling pump |
| Dry running, blockage, operating point drift | Load level and stability versus learned baseline | Good | — |
| Early seal wear | Only visible once leakage changes the load | Limited | — |
We state the limits up front, so monitoring is set up where it adds most and paired with the right complementary checks.
Below about 20 Hz on a VFD, or on slow-running pump bearings, signatures can fall under reliable thresholds.
Seal degradation without a measurable load change leaves little electrical trace. Use process instrumentation or visual checks.
Gradual material loss without hydraulic impact may not be observable until it affects performance.
For fine bearing assessment, a portable vibration check adds sensitivity — without permanent sensors.
Observed patterns are interpreted by qualified personnel; they guide investigation rather than replace it. AMTPro spot tests need near-constant speed (±1% frequency) during capture.
Artesis Insight turns spectra and trends into plain-language explanations and next steps. Your team does not need to be an ESA specialist to act on a result.
A cavitation signature in the spectrum suggests cavitation. Check suction pressure, NPSH margin and strainer condition, and compare with the pump’s operating point.
After action
Compare the trend under similar flow and load, and confirm with a vibration measurement if needed.
Example wording only; not a live alarm or a reproduced AI report.
No. Artesis measures three-phase current and voltage in the motor control cabinet. Nothing is mounted on the pump, motor or pipework.
Yes. e-MCM supports VFD applications. The initial baseline is best established at a fixed speed for about five hours before multi-speed learning is added. Diagnostic confidence is reduced below roughly 20 Hz.
Yes. The cases on this page include pumps at 6.6 kV and 11 kV, measured through the existing CT and PT secondaries.
It complements it. ESA gives a continuous, global view of motor and pump health from the panel. Portable vibration testing remains useful to confirm specific bearing or hydraulic findings.
Motor nameplate data, pump type, typical operating current, the starter or drive arrangement and the available measurement points in the cabinet.
Tell us about your pumps and drives. We’ll suggest where online monitoring pays off and where portable testing is enough.
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