Estudio de caso: Prevención de fallas en el motor de la bomba de lodo mediante detección temprana con DataMind AI™
septiembre 11, 2025
Overview
Razor Labs DataMind AI™ was monitoring critical rotating equipment at a vanadium mining site, including one of the site’s main slurry pumps. This pump is a critical component in one of the site’s two parallel processing lines. A failure would have forced a shutdown of the entire line, resulting in the loss of 50% of the site’s production capacity.
From the moment of deployment, DataMind AI™ flagged the pump motor as ״Alarm״, due to abnormally high and consistent friction indicators. Despite no operational symptoms at that stage, such as temperature increase or pressure drop – the system identified irregular vibration and friction patterns not typical for this pump.
The site team verified that the lubrication system was functioning correctly, and when no improvement was observed, they reduced the pump load to 60% in an attempt to stabilize its condition and prolong its life. DataMind AI™, however, continued reporting abnormal friction. These AI-driven insights prompted the team to order spare parts in advance. Toward the end of the month, when the parts arrived, the team conducted a planned shutdown and replaced the motor. Upon removal, a visible crack was discovered in the motor shaft – validating the early warning and confirming that failure had been imminent.
Detection & Diagnosis
DataMind AI™ monitored and classified the pump motor as high-risk based on:
- Persistently high-frequency vibration across all loads, indicative of friction
- Current and speed indicate that the motor was not operating under high load
Detection
Between mid-June and early July, following a gearbox replacement, DataMind AI™ detected a continuous rise in RMS vibration levels across multiple gearbox sensors. This time-domain trend clearly indicated progressive mechanical deterioration, well before any functional symptoms appeared.
Complementing the trend analysis, frequency-domain diagnostics revealed a sharp peak at 8605 Hz, corresponding to the gear mesh frequency (GMF). Distinct sidebands were observed at 50 Hz and 252 Hz, matching the known rotational speeds of the gearbox shafts. These spectral features are strong indicators of gear friction and misalignment under load – consistent with developing gear surface damage.
Vibration trend analysis shows increased levels:
Action & Response
Following the trend escalation, DataMind AI™ issued a critical alert and recommended inspection of the gearbox.
Razor Labs’ condition monitoring specialists reviewed the signal characteristics with the site’s maintenance team and confirmed the suspected damage location and mechanism
Before vs. After Replacement – Friction Resolved
Before replacement: Persistent high vibration levels linked to friction, even under reduced load
After replacement: Vibration levels returned to nominal ranges expected for this pump type
Gear wear visible on one side of the bull gear, indicating friction-related degradation
Conclusion
This case demonstrates how DataMind AI™ empowers teams to stay ahead of failure by translating subtle machine behavior into clear, actionable insights.
Instead of reacting to an unexpected motor breakdown (an event that would have halted one of only two processing lines), the site was able to plan ahead. The team secured spare parts, scheduled a controlled shutdown, and executed the replacement without disrupting production, avoiding ~$120,000 in production losses.
