Maintenance timed to real behavior, not a fixed calendar.
Every recommended service window is tied to a specific reading. The window narrows as an anomaly persists, and widens when it resolves.
From anomaly to a recommended window
A flagged anomaly on its own is useful. A recommended service window is more useful. The engine combines the shape of the current anomaly with the failure timelines seen on similar equipment - both across the customer fleet and across the wider pattern library - to propose when a machine should be looked at.
Recommendations come with a stated window, not a single date. A 24-48 hour window means the operator can slot the work into the next available planned stop within that range, rather than reacting to a hard alert that landed at an inconvenient moment.
Escalation paths
Every recommendation carries a severity tier that maps to a configurable escalation. On many production floors this looks like: maintenance-lead email for a 7-day advisory, on-call SMS for a 48-hour window, plant-manager acknowledgement required for anything shorter.
Being honest about accuracy
Predictive timing is at its most accurate on equipment types the engine has seen many failure examples of. On common induction motors, centrifugal pumps and standard industrial air compressors, predicted windows are reliable enough to schedule against directly. On rarer or highly customised equipment, predictions start out wider and tighten as the engine sees the specific fleet in operation.
This is stated clearly on every predicted-window callout. The confidence tier - along with the readings that drove it - is visible to whoever is deciding whether to act.
Pick an equipment type. Watch its sensor pulse move.
This is not a mockup. The waveforms below are being generated the same way we score real streams - learned envelope, drift detection, diagnosed callout. Pick a machine and watch an anomaly emerge.