Remaining Useful Life (RUL)
Remaining useful life is the estimated time an asset can keep operating before it fails or needs intervention. Predictive models estimate RUL from condition and operating data so maintenance and spares can be planned at the right moment.
RUL turns a fault detection into a planning number: not just 'this bearing is degrading' but 'about how long until it must be replaced'. Estimating RUL well lets teams prioritise the assets most likely to fail across a large fleet, order parts just in time, and avoid both early replacement and unplanned failure. Accuracy depends on data quality and how predictable the failure mode is.
Related terms
Predictive Maintenance (PdM) · Condition Monitoring
Related guides
Software
Siemens Senseye Predictive Maintenance
Scalable predictive maintenance that learns from existing condition data.
AVEVA Predictive Analytics
Early-warning analytics for critical process and power assets.