How Does Humaware Estimate Remaining Useful Life?
The earliest detection of events is essential for the implementation of predictive/prescriptive maintenance in order to maximise the remaining useful life of the asset. Using alerts generated by the Adaptive Anomaly Detector, we combine known information with supervised AI learning to produce remaining useful life estimates.
Humaware incorporates a Golden Database which contains previous knowledge and characteristics of known failure modes or events together with the maintenance action taken for these events. Any anomalies being detected are compared to the database using supervised learning AI technology. Where there is sufficient confidence of a pattern match then a remaining useful life (RUL) can be provided based on a probabilistic estimate of time to operational failure. Reliable RUL is a critical input for achieving an effective maintenance schedule.