Prediction methods and systems for structural repair during heavy maintenance of aircraft

A hybrid feature selection system to generate a predictive model for structural repair during heavy maintenance in a fleet of aircraft that each includes a structural component, the hybrid feature selection system comprising:

• a flight database of data collected by one or more aircraft sensors during flights of aircraft of the fleet of aircraft, the data including one or more of environmental conditions, aircraft operations, aircraft stresses, and overstress events experienced by individual aircraft of the fleet of aircraft during flights;
• at least one processing unit;
• a computer-readable memory storing non-transitory computer-readable instructions that, when executed by the at least one processing unit, cause the hybrid feature selection system to:
• retrieve qualification data from the flight database;
• calculate a qualification dataset by applying fatigue-related parameters to the qualification data;
• display the qualification dataset;
• receive, from a user, a selection of the fatigue-related parameters, wherein the qualification data is data collected during a first group of flights of a first aircraft of the fleet that experienced a replacement of the structural component during heavy maintenance of the first aircraft, wherein each flight of the first group of flights occurred before the heavy maintenance of the first aircraft, and wherein, during each flight of the first group of flights, the first aircraft experienced a corresponding overstress event for the first aircraft;
• retrieve verification data from the flight database;
• calculate a verification dataset by applying the selection of the fatigue-related parameters to the verification data;
• display the verification dataset;
• receive, from the user, a set of the selection of the fatigue-related parameters that is less than all of the fatigue-related parameters, wherein the verification data is data collected during a second group of flights of a second aircraft of the fleet that experienced heavy maintenance without replacement of the structural component of the second aircraft, wherein each flight of the second group of flights occurred before the heavy maintenance of the second aircraft, and wherein, during each flight of the second group of flights, the second aircraft experienced a corresponding overstress event for the second aircraft;
• retrieve training data from the flight database;
• calculate a training dataset by applying the set of the selection of the fatigue-related parameters to the rain ng data;
• train a predictive model for structural repair during heavy maintenance with the training dataset;
• and
• provide the predictive model to the user.

A method of determining a performance status of a structural component of an aircraft, the method comprising:

• storing first sensor data from one or more first aircraft sensors of the aircraft, the first sensor data including one or more of environmental conditions, aircraft operations, aircraft stresses, and overstress events experienced by one or more components of the aircraft;
• selecting a group of flights of the aircraft, wherein, during each flight, the aircraft experienced an overstress event;
• extracting feature data from a portion of the first sensor data flight data collected by the one or more first aircraft sensors during the group of flights, wherein the feature data relates to fatigue of the structural component;
• applying an ensemble of classifiers configured to identify categories to which the feature data belong to produce a classifier indicator for each classifier of the ensemble of classifiers of a predictive model, wherein each classifier is configured to indicate a category of the structural component of the aircraft based upon the feature data, and wherein the predictive model is trained using:
• a first data set from second sensor data collected by one or more third aircraft sensors of a second aircraft during a first group of flights of the first aircraft that experienced a replacement of the structural component during heavy maintenance of the first aircraft, wherein each flight of the first group of flights occurred before the heavy maintenance of the first aircraft and wherein, during each flight of the first group of flights, the first aircraft experienced a corresponding overstress event for the first aircraft, and
• a second data set from third sensor data collected by one or more third aircraft sensors of a second aircraft during a second group of flights of the second aircraft that experienced heavy maintenance without replacement of the structural component, wherein each flight of the second group of flights occurred before the heavy maintenance of the second aircraft and wherein, during each flight of the second group of flights, the second aircraft experienced a corresponding overstress event for the second aircraft;
• aggregating the classifier indicators to produce an aggregate indicator that indicates an aggregate category of the structural component;
• determining the performance status of the structural component based on the aggregate indicator;
• and
• initiating, based on the performance status, a signal to a health management system associated with the aircraft, the health management system being configured to update a maintenance schedule of the structural component based on the signal.