Predictive alarming lets you apply alarm levels on future sensor values, enabling the possibility to act in advance. Alarms on predicted values gives valuable input to operation teams, preventing potential problems from happening, thereby increasing uptime and prolonging machinery life.
Ekkono’s SDK enables predictive alarming by letting a machine learning model predict future sensor values. Setting alarm levels on predicted future values, rather than current sensor readings, enables the possibility to act before the real sensor values reach critical levels, granting a smooth and stable operation.
A predictive alarm model can be trained using available lab data, or incrementally on the device by feeding it streaming real-time data, allowing you to leverage the data you already have at hand, or hit the ground running when you have none.
When a problem is detected it may be too late to act.
Train a machine learning model to be able to predict a future sensor value. Using incremental learning, the result is a unique predictive alarm system for each individual device.
Send an alarm when the predicted sensor value is outside safety limits, triggering manual inspection of data or device.