Algorithm 3 1-FCL CNN -LSTM training algorithm

Input: C-MAPSS dataset(FD001, FD003) Output: 1-FCLCNN -LSTM model based on weight determination.

Feature selection of data and standardization of data. Prepare training set and test set; normalization processing of training set and test set Calculate the test set label. Extract input data of the network; Use a sliding window to split the data (Define the sliding window function to extract features). Use sliding window to extract training set label and test set label. Use Adam optimization algorithm to update weights. When the number of training periods of 1-FCLCNN -LSTM model is less than the set value or does not reach Early Stopping condition, the extracted features will be input into 1-FCLCNN -LSTM for forward propagation and the predicted output will be obtained. Calculate the error between the predicted value and the actual value. Obtain the trained 1-FCLCNN-LSTM model.