Problem
Industrial operators run expensive equipment on fixed maintenance schedules — maintaining things that don't need it and missing early signs of failure in equipment that does. Unplanned downtime in energy and automotive manufacturing costs millions per hour and is almost entirely preventable with adequate sensor instrumentation and AI.
Solution
A sensor data pipeline feeding real-time telemetry from equipment to anomaly detection models. When the model detects a signature associated with impending failure (vibration patterns, temperature drift, pressure anomalies), it triggers a work order in the CMMS with a failure probability score and recommended action window.
Outcome
Maintenance teams shift from schedule-driven to condition-driven operations. Equipment runs longer between interventions, failure events drop significantly, and maintenance budgets are allocated to the assets that actually need attention.