Abstract: A method is provided for diagnosing a failure on an aircraft that includes aircraft systems and monitors configured to report effects of failure modes of the aircraft systems. The method includes receiving a fault report that indicates one or more of the monitors that reported the effects of a failure mode in an aircraft system of the aircraft systems, and accessing a fault pattern library that describes relationships between possible failure modes and patterns of those of the monitors configured to report the effects of the possible failure modes. The method also includes diagnosing the failure mode of the aircraft system from the one or more of the monitors that reported, and using the fault pattern library and a greedy selection algorithm, determining a maintenance action for the failure mode; and generating a maintenance message including at least the maintenance action.

Abstract: A method is provided for diagnosing a failure on an aircraft that includes aircraft systems and monitors configured to report effects of failure modes of the aircraft systems. The method includes receiving a fault report that indicates one or more of the monitors that reported the effects of a failure mode in an aircraft system of the aircraft systems, and accessing a fault pattern library that describes relationships between possible failure modes and patterns of those of the monitors configured to report the effects of the possible failure modes. The method also includes diagnosing the failure mode of the aircraft system from the one or more of the monitors that reported, and using the fault pattern library and a greedy selection algorithm, determining a maintenance action for the failure mode; and generating a maintenance message including at least the maintenance action.

Abstract: In an example, a method for determining whether to perform aircraft maintenance is described. The method comprises selecting groupings of sensors and/or parameters associated with an aircraft type. The method comprises receiving feature data that corresponds to each grouping of sensors and/or parameters. The method comprises determining, from the feature data, values for predetermined operational metrics. The method comprises comparing the values to values for predetermined operational metrics that correspond to at least one other flight of the aircraft. The method comprises determining, based on comparing the values for the predetermined operational metrics, values of additional operational metrics. The method comprises training a machine learning model using at least the values for the additional operational metrics.

Abstract: Method and apparatus for detecting anomalous flights. Embodiments collect sensor data from a plurality of sensor devices onboard an aircraft during a flight. Feature definitions are determined, specifying a sensor device and an algorithm for deriving data values from sensor data collected from the device. Embodiments determine whether anomalous activity occurred during the flight using an anomaly detection model. An anomaly is detected including at least one of (i) a contextual anomaly where a data instance of a plurality of data instances is anomalous relative to a specific context, or (ii) a collective anomaly where two or more data instances are anomalous relative to a remainder of the plurality of data instances, even though each of the two or more data instances is not anomalous in and of itself. A report specifying a measure of the anomalous activity for the flight is generated.

Abstract: Method and apparatus for detecting anomalous flights. Embodiments collect sensor data from a plurality of sensor devices onboard an aircraft during a flight. Feature definitions are determined, specifying a sensor device and an algorithm for deriving data values from sensor data collected from the device. Embodiments determine whether anomalous activity occurred during the flight using an anomaly detection model. An anomaly is detected including at least one of (i) a contextual anomaly where a data instance of a plurality of data instances is anomalous relative to a specific context, or (ii) a collective anomaly where two or more data instances are anomalous relative to a remainder of the plurality of data instances, even though each of the two or more data instances is not anomalous in and of itself. A report specifying a measure of the anomalous activity for the flight is generated.

Abstract: In an example, a method for determining whether to perform aircraft maintenance is described. The method comprises selecting groupings of sensors and/or parameters associated with an aircraft type. The method comprises receiving feature data that corresponds to each grouping of sensors and/or parameters. The method comprises determining, from the feature data, values for predetermined operational metrics. The method comprises comparing the values to values for predetermined operational metrics that correspond to at least one other flight of the aircraft. The method comprises determining, based on comparing the values for the predetermined operational metrics, values of additional operational metrics. The method comprises training a machine learning model using at least the values for the additional operational metrics.

Abstract: Method and apparatus for detecting anomalous flights. Embodiments collect sensor data from a plurality of sensor devices onboard an aircraft during a flight. A plurality of feature definitions are determined, where a first one of the feature definitions specifies one or more of the plurality of sensor devices and an algorithm for deriving data values from sensor data collected from the one or more sensor devices. Embodiments determine whether anomalous activity occurred during the flight using an anomaly detection model, where the anomaly detection model describes a pattern of normal feature values for at least the feature definition, and comprising comparing feature values calculated from the collected sensor data with the pattern of normal feature values for the first feature definition. A report specifying a measure of the anomalous activity for the flight is generated.

Abstract: Method and apparatus for detecting anomalous flights. Embodiments collect sensor data from a plurality of sensor devices onboard an aircraft during a flight. A plurality of feature definitions are determined, where a first one of the feature definitions specifies one or more of the plurality of sensor devices and an algorithm for deriving data values from sensor data collected from the one or more sensor devices. Embodiments determine whether anomalous activity occurred during the flight using an anomaly detection model, where the anomaly detection model describes a pattern of normal feature values for at least the feature definition, and comprising comparing feature values calculated from the collected sensor data with the pattern of normal feature values for the first feature definition. A report specifying a measure of the anomalous activity for the flight is generated.

Abstract: A method for creating a generated data set(s) for use in process capability calculations that mimics the statistics of a raw data set is presented. The method includes the steps of inputting desirable statistics and generating numbers from theoretical statistical distributions. This generated data can be combined with other generated data and used to calculate global process capabilities. In addition, the global process capabilities can be used to accept or reject supplier(s) from a list of suppliers. An apparatus for implementing the method outlined above comprising raw data statistics, number generating algorithm(s) and storage medium(s) is also presented.