Method, System and Storage Medium for Remaining Useful Life Prediction of Aircraft Engine Based on Gaussian Process Regression Integrated Deep Learning

A system, comprising:

• a memory, configured to store program instructions for performing a method for remaining useful life (RUL) prediction of an aircraft engine based on gaussian process regression (GPR) integrated deep learning (GIDL);
• and
• a processor, coupled with the memory and, when executing the program instructions, configured for:
• partitioning observation data into training data, validation data, and testing data;
• training a generative GPR model using the training data to obtain a trained GPR model;
• using the trained GPR model as a synthetic data generator to generate synthetic data;
• performing an averaging process to integrate the synthetic data and the training data to obtain integrated data;
• generating a plurality of data minibatches from the integrated data;
• feeding the plurality of data minibatches into a deep leaning model to train the deep leaning model;
• obtaining RUL prediction from the trained deep learning model based on the validation data;
• and
• using the RUL prediction for further parameter training of the generative GPR model and the deep learning model.

A non-transitory computer-readable storage medium, containing program instructions for, when being executed by a processor, performing a method for remaining useful life (RUL) prediction of an aircraft engine based on gaussian process regression (GPR) integrated deep learning (GIDL), the method comprising:

• partitioning observation data into training data, validation data, and testing data;
• training a generative GPR model using the training data to obtain a trained GPR model;
• using the trained GPR model as a synthetic data generator to generate synthetic data;
• performing an averaging process to integrate the synthetic data and the training data to obtain integrated data;
• generating a plurality of data minibatches from the integrated data;
• feeding the plurality of data minibatches into a deep leaning model to train the deep leaning model;
• obtaining RUL prediction from the trained deep learning model based on the validation data;
• and
• using the RUL prediction for further parameter training of the generative GPR model and the deep learning model.