Abstract: Methods and systems for detecting and correcting anomalies include detecting an anomaly in a cyber-physical system, based on a classification of time series information from sensors that monitor the cyber-physical system as being anomalous. A similarity graph is determined for each of the sensors, based on the time series information. A subset of the sensors that are related to the classification is selected, based on a spectral embedding of the similarity graphs. A corrective action is performed responsive to the detected anomaly, prioritized according to the selected subset.

Abstract: Methods and systems for optimizing performance of a cyber-physical system include training a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained machine learning module and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.

Abstract: Methods and systems for detecting and correcting anomalies include ranking sensors in a cyber-physical system according to a degree of influence each sensor has on a measured performance indicator in the cyber-physical system. An anomaly is detected in the cyber-physical system based on the measured performance indicator. A corrective action is performed responsive to the detected anomaly, prioritized according to sensor rank.

Abstract: Methods and systems for detecting and responding to anomalous system behavior include detecting an anomaly in a cyber-physical system, based on a classification of time series information, from sensors that monitor the cyber-physical system, as being anomalous. A transition rule is extracted from the time series information to characterize a cause of the anomalous behavior, using a temporal gradient boosting tree. A corrective action is performed responsive to the detected anomaly, prioritized by the cause of the anomalous behavior.

Abstract: Systems and methods for predicting device failure, including inputting a plurality of records for electronic communication devices, each including one or more attributes and a label, as a table to a modeling algorithm, wherein there are separate tables for each period in a time sequence; building a multi-stage decision tree from the time sequence of records using the modeling algorithm running on a processor device; inputting a record for a device having an empty label value into the decision tree to determine the likelihood of entity failure; and reporting a predicted failure for the device to a user on a display to initiate replacement before a next time period.

Abstract: Methods and systems for detecting and correcting anomalies include detecting an anomaly in a cyber-physical system, based on a classification of time series information from sensors that monitor the cyber-physical system as being anomalous. A similarity graph is determined for each of the sensors, based on the time series information. A subset of the sensors that are related to the classification is selected, based on a spectral embedding of the similarity graphs. A corrective action is performed responsive to the detected anomaly, prioritized according to the selected subset.

Abstract: Methods and systems for detecting and correcting anomalies include ranking sensors in a cyber-physical system according to a degree of influence each sensor has on a measured performance indicator in the cyber-physical system. An anomaly is detected in the cyber-physical system based on the measured performance indicator. A corrective action is performed responsive to the detected anomaly, prioritized according to sensor rank.

Abstract: Methods and systems for optimizing performance of a cyber-physical system include training a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained machine learning module and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.