Method for predicting remaining useful life (rul) of aero-engine based on automatic differential learning deep neural network (adldnn)

data acquisition:

• acquiring multidimensional degradation parameters of an aero-engine to be predicted, analyzing a stable trend, and selecting a plurality of parameters capable of reflecting degradation performance of the aero-engine to obtain acquired data;

data preprocessing:

• segmenting the acquired data by a sliding window (SW) to obtain preprocessed data;

model construction:

• constructing a RUL prediction model of the aero-engine based on an ADLDNN, the RUL prediction model comprising a multibranch convolutional neural network (MBCNN) model, a multicellular bidirectional long short-term memory (MCBLSTM) model, a fully connected (FC) layer FC1, and a regression layer;

feature extraction:

• taking the preprocessed data as input data of the MBCNN model, extracting an output of the MBCNN model, taking the output of the MBCNN model and recursive data as input data of the MCBLSTM model, and extracting an output of the MCBLSTM model;
• and

RUL prediction:

• taking the output of the MCBLSTM model as an input of the FC layer FC1 to obtain an output of the FC layer FC1, and inputting the output of the FC layer FC1 to the regression layer to predict a RUL.