Method and system for performing automated root cause analysis of anomaly events in high-dimensional sensor data

An apparatus for identifying a root cause of an anomaly in operation of one or more machines, comprising:

• one or more processors;
• a set of sensors embedded in the one or more machines;
• and
• memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
• obtain sensor data from a set of sensors associated with the one or more machines;
• convert the sensor data into a set of sensor states;
• build an optimal Directed Acyclic Graph (DAG) for the set of sensors based on the set of sensor states to model causal dependency;
• determine, by using the DAG, a probability of an anomaly state of a target sensor based on a state of a direct neighbor sensor;
• and
• determine a root cause of the anomaly state associated with the target sensor by back-tracking the anomaly state in the DAG.

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for identifying a root cause of an anomaly in operation of one or more machines, the method comprising:

• obtaining sensor data from a set of sensors associated with the one or more machines;
• converting the sensor data into a set of sensor states;
• building an optimal Directed Acyclic Graph (DAG) for the set of sensors based on the set of sensor states to model causal dependency;
• determining, by using the DAG, a probability of an anomaly state of a target sensor based on a state of a direct neighbor sensor;
• and
• determining a root cause of the anomaly state associated with the target sensor by back-tracking the anomaly state in the DAG.