Remaining useful life prediction of variable-operating turbofan engine based on VMD-CNN-BiLSTM

In order to address the issue of low prediction accuracy in traditional forecasting methods for residual life of turbofan engines under variable working conditions, a variational mode decomposition convolutional neural network bidirectional long short term memory (VMD-CNN-BiLSTM) model is proposed. Firstly, variational mode decomposition (VMD) is used to normalize the data and split it into sub-data at predetermined intervals. This allows for the thorough extraction of hidden temporal features in multidimensional data as well as the removal of singular samples and dimensional variations. Secondly, a VMD-CNN-BiLSTM model is constructed for predicting the residual life of turbofan engines under variable working conditions. The convolutional neural network (CNN) is employed for feature extraction and fusion to generate multiple mappings. These mappings are then input into the BiLSTM network to capture time dependencies in the time series data and produce accurate predictions of remaining engine life. Finally, hyperparameter optimization using the Sparrow algorithm enhances the prediction performance of the model. As shown by root mean squared error (RMSE) values of 13.74±0.51 and mean absolute error (MAE) values of 11.24±0.49 when predicting remaining engine life under variable operating conditions, experimental results on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset show that VMD-CNN-BiLSTM achieves high accuracy and generalization performance even with noisy data.