Advanced analytic methods and systems utilizing trust-weighted machine learning models

A computerized system comprising:

• a computer-readable memory;
• a processing unit operatively coupled to the computer-readable memory;
• and
• a computer-readable storage media assemblage, wherein the storage media assemblage is operatively coupled to the computer-readable memory and includes instructions, that when executed by the processing unit, cause the system to:
• extract feature data from flight data collected by sensors during a flight of the aircraft, wherein the feature data relates to performance of one or more components of the aircraft;
• apply an ensemble of related machine learning models to produce, for each model, a positive score and a complementary negative score related to performance of the selected component, wherein each model is characterized by a false positive rate and a false negative rate;
• weight the positive score of each model to produce a weighted positive score for each model based on the false positive rate of that model such that the weighted positive score of each model is anti-correlated with the false positive rate of that model;
• weight the negative score of each model to produce a weighted negative score for each model based on the false negative rate of that model such that the weighted negative score of each model is anti-correlated with the false negative rate of that model;
• and
• determine a performance status of the selected component as one of:
• a positive category when an average of the weighted positive scores of the models of the ensemble of related machine learning models is greater than a threshold value and an average of the weighted negative scores of the models of the ensemble of related machine learning models is less than or equal to the threshold value;
• a negative category when the average of the weighted positive scores is less than or equal to the threshold value and the average of the weighted negative scores is greater than the threshold value;
• or
• an unclassified category when both of the average of the weighted positive scores and the average of the weighted negative scores are greater than the threshold value or both are less than or equal to the threshold value.

A system for determining a performance status of a selected component in an aircraft, the system comprising:

• a feature extraction module configured to extract feature data from flight data collected by sensors during a flight of the aircraft, wherein the feature data relates to performance of one or more components of the aircraft;
• a performance classification module configured to determine a performance status of the selected component of the aircraft based on the feature data, wherein the performance classification module is programmed to:
• apply an ensemble of related machine learning models to produce, for each model, a positive score and a complementary negative score related to performance of the selected component, wherein each model has an ROC (receiver operating characteristic) characterized by a false positive rate and a false negative rate;
• weight the positive score of each model to produce a weighted positive score for each model based on the false positive rate of that model such that the weighted positive score of each model is anti-correlated with the false positive rate of that model;
• weight the negative score of each model to produce a weighted negative score for each model based on the false negative rate of that model such that the weighted negative score of each model is anti-correlated with the false negative rate of that model;
• and
• determine the performance status of the selected component as one of:
• a positive category when an average of the weighted positive scores of the models of the ensemble of related machine learning models is greater than a threshold value and an average of the weighted negative scores of the models of the ensemble of related machine learning models is less than or equal to the threshold value;
• a negative category when the average of the weighted positive scores is less than or equal to the threshold value and the average of the weighted negative scores is greater than the threshold value;
• or
• an unclassified category when both of the average of the weighted positive scores and the average of the weighted negative scores are greater than the threshold value or both are less than or equal to the threshold value.