Abstract: A method and apparatus are described. The method includes receiving a set of data streams including data values generated by a sensor associated with the operation of a component in a system at points in time and generating an anomaly data value for the received data values. The method further includes applying a machine learning algorithm to the received data values and a subset of data values previously received to generate expected data values at points in time beyond the current point in time, generating an expected anomaly data value for each of the expected data values, and identifying an operational anomaly for the component at a point in time beyond the current time based on the expected anomaly data value. The apparatus includes an input interface for receiving the data streams and a processor for processing the received data values to identify an operational anomaly as described above.

Abstract: A method, apparatus, and system are described. The method includes generating a set of current values associated with at least one component included on a moving vehicle and providing the set of current values over a wireless network. The values are generated by one or more sensors. An edge computing device receives the current values. The method further includes processing the set of current values in real-time using at least one machine learning algorithm to identify a value of a point in time for a failure of one of the at least one component based on the set of current values and at least one set of past values received. The past values are stored in a memory. The set of current values are transmitted with a low time latency between the generating the set of current values and the processing of the set of current values.