CPC G06F 16/2474 (2019.01) [G06F 11/079 (2013.01); G06F 11/3447 (2013.01); G06F 11/3452 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01)] | 20 Claims |
1. A method for anomaly detection, the method comprising:
receiving, by one or more processors, a first set of time series from a data source, wherein the data source provides sensor data and a timestamping of the sensor data as the first set of time series, the sensor data comprising values of a first group of measurands;
identifying, by one or more processors, unexpected values of monitoring measurands in a monitored time series utilizing an anomaly detection algorithm;
determining, by one or more processors, that values of a second group of one or more of the measurands of a subset of the received sensor data indicates an anomaly by executing the anomaly detection algorithm on the received time series;
sending, by one or more processors, anomalous data indicative of the subset of sensor data to a root cause analysis system;
receiving, by one or more processors, a root cause analysis feedback from the root cause analysis system, the root cause analysis feedback being indicative of a result of a root cause analysis of the subset of sensor data, the root cause analysis feedback comprising a third group of the measurands; and
training, by one or more processors, the anomaly detection algorithm based on a difference between the third groups of measurands and the second groups of measurands to predict values of monitoring measurands deviating from a normal behavior.
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