Precognize’s advanced machine learning methods separate between normal and abnormal data points.
Using a year's worth of historical data as the plant's "baseline", the software creates a statistical model to inspect new data in real-time.
Precognize rapidly captures the way systems are built and behave. Precognize's drag and drop interface allows operational experts with no modeling training to accurately describe their systems. The software creates a complete model in a matter of a few days to a couple of weeks, depending on the number of sensors.
Precognize converts the conceptual model of the plant into a graph. Abnormal data points are compared using several graph techniques to detect where problems in the system originate. Once discovered, operators are presented with clear, specific true alerts.
Anomalies in a plant are frequent events. Faulty sensor readout and functional imperfections show up as alerts, distracting operators from detecting events that cause equipment failure.
Through machine learning and conceptual modeling, Precognize eliminates statistical anomalies, while singling out critical events long before they happen.