Abstract: Predictive aircraft maintenance systems and methods are disclosed. Predictive maintenance methods may include extracting feature data from flight data collected during a flight of the aircraft, applying an ensemble of related classifiers to produce a classifier indicator for each classifier of the ensemble of classifiers, aggregating the classifier indicators to produce an aggregate indicator indicating an aggregate category of a selected component for a threshold number of future flights, and determining the performance status of the selected component based on the aggregate indicator. The classifiers are each configured to indicate a category of the selected component within a given number of flights. The given number of flights for each classifier is different. The threshold number of future flights is greater than or equal to the maximum of the given numbers of the classifiers.

Abstract: Systems and methods of the present disclosure include determining a performance status of a selected component in an aircraft. An ensemble of related machine learning models is applied to feature data extracted from flight data of the aircraft. Each model produces a positive score and a complementary negative score related to performance of the selected component. The positive scores are weighted based on the false positive rates of the models and the negative scores are weighted based on the false negative rates of the models. The weighted positive scores are combined, e.g., by averaging, and the weighted negative scores are combined, e.g., by averaging. The performance status of the selected component is determined as one of a positive category, a negative category, or an unclassified category based on the values of the combined weighted positive scores and the combined weighted negative scores.

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: Hybrid feature selection methods include methods of creating a predictive model for structural repair during heavy maintenance in a fleet of aircraft. Methods include qualifying a qualification dataset of fatigue-related parameters calculated from data collected during a first group of flights of a first aircraft that experience a replacement of a structural component during heavy maintenance. Methods include receiving a qualified selection of the fatigue-related parameters and verifying a verification dataset of the qualified selection of the fatigue-related parameters calculated from data collected during a second group of flights of a second aircraft that experienced heavy maintenance without replacement of the structural component.

Abstract: Predictive aircraft maintenance methods include extracting feature data from flight data collected during a flight of the aircraft, calculating a performance classifier indicator that indicates a performance category of the selected flight control surface component within a threshold number of future flights based on the feature data, and determining the performance status of the selected flight control surface component relative to the threshold number of future flights based on the performance classifier indicator. Such methods may include classifying the feature data with an ensemble of related primary classifiers to produce a primary classifier indicator for each primary classifier and aggregating the primary classifier indicators to produce the performance classifier indicator indicating the performance category of the selected active component for the threshold number of future flights.

Abstract: Predictive aircraft maintenance systems and methods are disclosed. Predictive maintenance methods may include extracting feature data from flight data collected during a flight of the aircraft, applying an ensemble of related classifiers to produce a classifier indicator for each classifier of the ensemble of classifiers, aggregating the classifier indicators to produce an aggregate indicator indicating an aggregate category of a selected component for a threshold number of future flights, and determining the performance status of the selected component based on the aggregate indicator. The classifiers are each configured to indicate a category of the selected component within a given number of flights. The given number of flights for each classifier is different. The threshold number of future flights is greater than or equal to the maximum of the given numbers of the classifiers.

Abstract: Machine learning systems and computerized methods to compare candidate machine learning algorithms are disclosed. The machine learning system comprises a machine learning algorithm library, a data input module to receive a dataset and a selection of machine learning models derived from the machine learning algorithm library, an experiment module, and an aggregation module. The experiment module is configured to train and evaluate each machine learning model to produce a performance result for each machine learning model. The aggregation module is configured to aggregate the performance results for all of the machine learning models to form performance comparison statistics.