| CPC G06F 30/27 (2020.01) [G06N 20/00 (2019.01); G06F 2119/02 (2020.01)] | 20 Claims |
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1. A method for generating an augmented reliability performance model for a product, the method comprising:
obtaining a reliability performance model for the product;
developing a reliability prediction machine learning model for predicting reliability performance of the product based on data obtained from manufacturing and testing of the product;
implementing the reliability prediction machine learning model in a production environment for the product;
determining an effectiveness of the reliability prediction machine learning model based on manufacturing and testing of the product in the production environment;
adjusting the reliability prediction machine learning model based on the effectiveness;
obtaining feature names for the reliability prediction machine learning model and their predictive power values, wherein the feature names correspond to features from the data obtained from manufacturing and testing of the product;
extracting a set of feature names corresponding to features having highest predictive power values from the feature names; and
generating the augmented reliability performance model for the product by modifying the reliability performance model to incorporate one or more model parameters derived from the set of feature names.
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