Abstract: The present invention relates to a prediction method for stall and surge of an axial compressor based on deep learning. The method comprises the following steps: firstly, preprocessing data with stall and surge of an aeroengine, and partitioning a test data set and a training data set from experimental data. Secondly, constructing an LR branch network module, a WaveNet branch network module and a LR-WaveNet prediction model in sequence. Finally, conducting real-time prediction on the test data: preprocessing test set data in the same manner, and adjusting data dimension according to input requirements of the LR-WaveNet prediction model; giving surge prediction probabilities of all samples by means of the LR-WaveNet prediction model according to time sequence; and giving the probability of surge that data with noise points changes over time by means of the LR-WaveNet prediction model, to test the anti-interference performance of the model.

Abstract: The present invention provides a spatiotemporal dynamic system soft sensing method for automatically determining a partial differential equation (PDE) structure and belongs to the technical field of soft sensing of neural networks. Firstly, a loss function for training a coupled physics-informed neural network with a recurrent prediction mechanism is constructed to obtain a solution and a driving source which satisfy a PDE used for describing spatiotemporal industrial processes; secondly, differential operator candidates are obtained by an automatic differentiation method, and an appropriate PDE structure is selected from the differential operator candidates to accurately describe the spatiotemporal industrial processes; and finally, the soft sensing result is verified using heat diffuse phenomena and actual vibration processes.

Abstract: A coupled physics-informed neural network for solving displacement distribution of a bounded vibration string under an unknown external driving force is provided. A novel PINN is proposed, called C-PINN, used for solving the displacement distribution of the bounded vibration string under an external driving force with little or even no priori information. It comprises two neural networks: NetU and NetG. NetU is used for approximating satisfying the displacement distribution of the bounded vibration string under study. NetG is used for regularizing u in the NetU to satisfy the displacement distribution of the approximation of NetU. The two networks are integrated into a data-physics-hybrid loss function. In addition, a proposed hierarchical training strategy is used for optimizing the loss function and realizing the coupling of the two networks. Finally, the performance of the C-PINN in solving the displacement distribution of the bounded vibration string under the external driving force is verified.

Abstract: The present invention provides an optimization algorithm for automatically determining variational mode decomposition parameters based on bearing vibration signals. First, mode energy is used to reflect bandwidth, a bandwidth optimization sub-model is established to automatically obtain optimal bandwidth parameter ?opt. Secondly, energy loss optimization sub-model is established to avoid under-decomposition. Thirdly, a mode mean position distance optimization sub-model is established to prevent the generation of too much K and avoid the phenomenon of over-decomposition.

Abstract: The present invention relates to a prediction method for stall and surge of an axial compressor based on deep learning. The method comprises the following steps: firstly, preprocessing data with stall and surge of an aeroengine, and partitioning a test data set and a training data set from experimental data. Secondly, constructing an LR branch network module, a WaveNet branch network module and a LR-WaveNet prediction model in sequence. Finally, conducting real-time prediction on the test data: preprocessing test set data in the same manner, and adjusting data dimension according to input requirements of the LR-WaveNet prediction model; giving surge prediction probabilities of all samples by means of the LR-WaveNet prediction model according to time sequence; and giving the probability of surge that data with noise points changes over time by means of the LR-WaveNet prediction model, to test the anti-interference performance of the model.