Predict Failures Before They Happen
AI models trained on your equipment data calculate Remaining Useful Life in real time. Plan maintenance at the right moment — not too early, not too late.
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cost reduced
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work orders
Complete Predictive Maintenance Stack
Vibration & Signature Analysis
FFT and envelope analysis on rotating equipment. Detects bearing defects, imbalance, misalignment, and looseness before symptoms appear.
Anomaly Detection
Unsupervised ML models learn normal equipment behavior and flag deviations with root-cause attribution across 100+ sensor tags.
Remaining Useful Life (RUL)
Degradation curves with confidence intervals show precisely how much life remains. Maintenance windows planned with data, not guesswork.
CMMS Integration
Automatic work order generation in your existing CMMS when RUL crosses the threshold. Zero manual intervention required.
Remaining Useful Life, in days not hours.
Continuous degradation tracking on every monitored asset — observed history, forecast envelope, and time-to-failure estimate.
Reactive Maintenance Is Expensive and Unpredictable
Break-fix maintenance creates production chaos. Preventive schedules waste resources. Neither approach uses the data your equipment is already generating.
Unplanned Stops Cascade
One unexpected failure stops not just one machine — it cascades through the line. Downstream production is lost for hours or days.
Preventive Waste
Changing parts on schedule wastes up to 30% of maintenance budget on components that still have useful life.
Data Already Exists — Unused
Vibration, temperature, current, and pressure sensors are already installed. Without ML models, their data is never fully exploited.
Ready to Stop Reacting to Failures?
Book a demo and see how InduStream predicts equipment degradation on your actual machinery.