Predict the Failure. Plan the Fix.
Avoid Unexpected Downtime.
Your maintenance schedule shouldn't be based on time intervals when it can be based on actual equipment condition. Brains monitors your rotating assets continuously — compressors, pumps, motors — and predicts failures 15+ hours before they happen, with the engineering context that explains why.
Book a DemoMaintenance When Its Needed,
Not When Its Scheduled.
Calendar-based maintenance replaces equipment on schedule — whether it needs it or not. Condition-based maintenance replaces equipment when the data says it needs replacing. Brains provides the data, the diagnosis, and the engineering context to make that decision with confidence.
Evidence Based Maintenance With Signals.
Continuous Rotating Asset Monitoring
Compressors, pumps, motors, turbines — monitored continuously through operating patterns, not just vibration thresholds. Brains learns each asset's normal behavior and detects degradation patterns as they develop, not after they cause a failure.
Failure Prediction With Engg. Context
A bearing degradation pattern means different things on different equipment. Brains knows the difference because it runs on the Contextual Graph — connecting the vibration signature to the equipment type, its design specifications, its operating conditions, and its maintenance history.
Model-Driven Time-to-Failure Estimation
Not just "this asset is degrading" — but "at the current rate, this asset will fail in approximately 72 hours." Your maintenance team schedules the intervention. The equipment runs until it actually needs attention. No premature replacements.
Multi-Signal Correlation
Equipment failure rarely announces itself through a single sensor. Brains correlates vibration, temperature, pressure, flow, and power consumption simultaneously — identifying the multi-signal patterns that precede failures before any single threshold is breached.
Maintenance Planning Integration
Brains delivers failure predictions with enough lead time for your maintenance team to plan — order parts, schedule crews, coordinate with operations. 15+ hours of advance warning turns emergency response into planned maintenance.
ESP & Artificial Lift Optimization
Electrical submersible pumps, gas lift systems, and rod pump operations — each with unique failure modes and set-point requirements. Brains monitors artificial lift equipment and optimizes set-points based on actual well performance, not static initial configurations.
Continuous Rotating Asset Monitoring
Compressors, pumps, motors, turbines — monitored continuously through operating patterns, not just vibration thresholds. Brains learns each asset's normal behavior and detects degradation patterns as they develop, not after they cause a failure.
Failure Prediction With Engg. Context
A bearing degradation pattern means different things on different equipment. Brains knows the difference because it runs on the Contextual Graph — connecting the vibration signature to the equipment type, its design specifications, its operating conditions, and its maintenance history.
Model-Driven Time-to-Failure Estimation
Not just "this asset is degrading" — but "at the current rate, this asset will fail in approximately 72 hours." Your maintenance team schedules the intervention. The equipment runs until it actually needs attention. No premature replacements.
Multi-Signal Correlation
Equipment failure rarely announces itself through a single sensor. Brains correlates vibration, temperature, pressure, flow, and power consumption simultaneously — identifying the multi-signal patterns that precede failures before any single threshold is breached.
Maintenance Planning Integration
Brains delivers failure predictions with enough lead time for your maintenance team to plan — order parts, schedule crews, coordinate with operations. 15+ hours of advance warning turns emergency response into planned maintenance.
ESP & Artificial Lift Optimization
Electrical submersible pumps, gas lift systems, and rod pump operations — each with unique failure modes and set-point requirements. Brains monitors artificial lift equipment and optimizes set-points based on actual well performance, not static initial configurations.
Every unnecessary scheduled shutdown costs production. Every unexpected failure costs more. The sweet spot is maintaining exactly when the equipment needs it — not before, not after. That's what condition-based maintenance delivers.
Frequently Asked Questions
Common questions about condition based maintenance.
Brains monitors rotating assets (compressors, pumps, motors, turbines), heat transfer equipment (heat exchangers, boilers, condensers), artificial lift systems (ESPs, gas lift, rod pumps), and process vessels. Any equipment with sensor data — vibration, temperature, pressure, flow, power consumption — can be monitored for condition-based maintenance.
Vibration monitoring detects mechanical issues through a single signal type. Brains correlates vibration with temperature, pressure, flow, power consumption, and process conditions simultaneously — and connects all of it to the equipment's engineering design through the Contextual Graph. A vibration increase on a pump means different things at different flow rates, different suction pressures, and different stages of bearing life. Brains knows the context.
Brains detects anomalies up to 15+ hours in advance of actual failure. The accuracy depends on the equipment type, the available sensor data, and the length of operating history. Predictions improve over time as the model learns from confirmed outcomes. Human-in-the-loop validation is built into the workflow — your reliability engineers confirm or override every prediction.
No. Brains feeds into your existing CMMS — providing the condition data and failure predictions that trigger work orders. Your maintenance planning process stays the same. What changes is the basis for scheduling: instead of calendar intervals or manufacturer recommendations, your work orders are triggered by actual equipment condition and predicted failure timelines.
Brains begins learning from the moment historian data is connected. A minimum of 3-6 months of historical data provides a baseline for normal behavior. Active learning improves continuously — every confirmed failure, every false positive reviewed, every maintenance outcome feeds back into the model. Most pilots demonstrate predictive value within the first 2-4 months.
Ready to Maintain by Condition Instead of Calendar?
Connect your asset data. See failure predictions with engineering context — 15+ hours before the failure arrives.
- Predict failures 15+ hours before they happen.
- Eliminate unnecessary scheduled shutdowns.
- Correlate vibration, temperature, pressure, and flow simultaneously.
- Get time-to-failure estimates your maintenance team can plan around.
- Achieve ROI in less than 12 months.