Asset Management
Predict equipment failures before they happen
Unplanned downtime costs millions. Reactive maintenance is expensive and disruptive. xplainable helps asset-heavy industries predict equipment failures with transparent ML so you can schedule maintenance proactively, extend asset life, and keep operations running smoothly.
Asset ID: PMC-2847
Immediate attention required
If you're experiencing...
Unexpected Equipment Failures
Critical assets fail without warning, halting production. Emergency repairs cost 3-5x more than planned maintenance.
Over-Maintenance
You replace parts on a fixed schedule whether they need it or not. Time-based maintenance wastes money on healthy components.
Siloed Sensor Data
You have mountains of IoT data but no way to turn it into actionable predictions. Data sits in warehouses, unused.
Black-Box Predictions
Existing predictive maintenance tools give alerts but no explanation. Operators don't trust recommendations they can't verify.
Slow Model Development
Building custom ML models takes months and requires specialised data scientists. By the time they're deployed, equipment has changed.
We can help!
xplainable's Asset Management solution ingests your sensor data, maintenance logs, and operational parameters to predict remaining useful life and failure probability. Unlike black-box systems, we explain which signals indicate trouble, such as rising temperature, vibration patterns, and usage anomalies, so maintenance teams trust and act on predictions.
With xplainable you will have...
Reduced Unplanned Downtime
Catch failures before they happen. Schedule maintenance during planned windows, not emergency shutdowns.
Optimised Maintenance Spend
Replace parts when they actually need it, not on arbitrary schedules. Condition-based maintenance cuts costs while improving reliability.
Extended Asset Life
Early intervention prevents cascading damage. Keep equipment running longer by addressing issues before they compound.
Trusted Predictions
Operators see why an asset is flagged, including specific sensors, trends, and thresholds. Transparent models build confidence and adoption.
Scalable Deployment
Train once, deploy across your fleet. xplainable models generalise to similar assets with minimal reconfiguration.
