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: A method for prognostic modeling includes obtaining probability values for possible health states of a system or component part using one or more data driven models and one or more physics of failure models. A probabilistic network is built using a plurality of observed and latent variables. The probable outcomes from the one or more physics of failure models and the one or more data driven models are combined to create an integrated model for failure prognosis. A health state of the system or system component is predicted using possible health states using the integrated model and the probabilistic network.

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.