Dynamic System Modeling of a Hybrid Neural Network with Phase Space Reconstruction and a Stability Identification Strategy

Abstract

Focusing on the identification of dynamic system stability, a hybrid neural network model is proposed in this research for the rotating stall phenomenon in an axial compressor. Based on the data fusion of the amplitude of the spatial mode, the nonlinear property is well characterized in the feature extraction of the rotating stall. This method of data processing can effectively avoid the inaccurate recognition of single or multiple measuring sensors only depending on pressure. With the analysis on the spatial mode, a chaotic characteristic was shown in the development of the amplitude with the first-order spatial mode. With the prerequisite of revealing the essence of this dynamic system, a hybrid radial basis function (RBF) neural network was adopted to represent the properties of the system by artificial intelligence learning. Combining the advantages of the methods of K-means and Gradient Descent (GD), the Chaos–K-means–GD–RBF fusion model was established based on the phase space reconstruction of the chaotic sequence. Compared with the two methods mentioned above, the calculation accuracy was significantly improved in the hybrid neural network model. By taking the strategy of global sample entropy and difference quotient criterion identification, a warning of inception can be suggested in advance of 12.3 revolutions (296 ms) with a multi-step prediction before the stall arrival.

Identification Strategy Design with the Solution of Wavelet Singular Spectral Entropy Algorithm for the Aerodynamic System Instability

“…When entering the rotating stall stage, the modal amplitude starts to fluctuate greatly. The same analysis is also confirmed in the literature [13], showing the repeatability of the reported results. It is concluded that the essential characteristic of the compressor system is captured by the increment in amplitude of modal energy in the gradual process of stall development.…”

“…The inception can be suggested by about 12-68 revolutions before the stall arrival. On this basis, a hybrid neural fusion network was proposed by Zhang to represent the properties of a compressor system by artificial intelligence learning [13]. By taking the identification strategy with sample entropy and a difference quotient criterion, an inception warning can be suggested in advance with a multi-step prediction of a neural network.…”

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum

“…The relevant geometric parameters of the compressor are presented in Table 1 for a brief synopsis. An introduction on the test rig is described in the references [ 15 ].…”

“…By the stall data training with Long Short Term Memory (LSTM), the regression model was adopted in the neural network of the compressor [ 14 ]. The rotating stall of the axial flow compressor was predicted based on the K-means-GD-RBF fusion model [ 15 ] reconstructed from the phase space of the chaotic sequence. The global sample entropy was employed to detect the stall precursor in advance according to the difference quotient discrimination strategy.…”

“…As the continuation of previous research [ 15 ], the main purpose of this paper is to seek a more reliable method to model the overall system of the compressor, especially for the short-scale vortex identification from the frequency domain perspective by machine learning. The concerns to be solved include the time-space resolved diagnosis for the parameters with small-scale and multi-frequencies, as well as the optimization of the network structure parameters.…”