Asset Failure Probability Scoring uses AI to estimate the probability of each critical asset failing within a defined time window, enabling maintenance to be scheduled before failure rather than after, by combining sensor telemetry, maintenance history, and asset age data into a failure probability estimate, across asset maintenance and operations workflows.
Maintenance teams schedule preventive maintenance at fixed intervals defined by manufacturer recommendations, without regard to the actual condition or operating environment of individual assets. Assets in good condition are serviced unnecessarily, while assets approaching failure through accelerated wear are missed between scheduled interventions.
The AI ingests sensor telemetry (vibration, temperature, pressure, current), maintenance records, and asset age and load history, and produces a failure probability score for each asset over a configurable time horizon. High-scoring assets are surfaced for condition-based intervention before the fixed maintenance schedule would trigger.
Unplanned downtime events per year decrease; maintenance spend per asset decreases.
No intrinsic risk triggered.
No controls triggered.
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