An integrated deep multiscale feature fusion network for aeroengine remaining useful life prediction with multisensor data

Most RUL prediction methods can only extract single-scale features, ignoring significant details at other scales and layers. These methods are all constructed using one type of model, and do not use the advantages of different models. An integrated deep multiscale feature fusion network (IDMFFN) for aeroengine RUL prediction using multisensor data is proposed in this study. Two-dimensional samples are constructed using multisensor data with multiple time cycles. Multiscale feature extraction blocks are designed to learn different-scale features using convolutional filters of different sizes. A multiscale feature concatenated block is constructed to integrate multiscale features from different layers. A GRU-based high-level feature fusion block is built to replace the traditional fully connected layer, and can leverage powerful temporal feature learning for feature fusion. A novel activation function Mish is used to construct the network. A simulated turbofan engine dataset was used to verify the effectiveness of the network. The results suggest that the IDMFFN can predict RUL more accurately than existing methods.