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

Wei Tuo,Mingming Zhang,Jia Zhang,Aiguo Xia,Anping Hou

doi:10.3390/machines10020122

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.

Highlights

As one of the most difficult problems in dynamic system stability, the rotating stall phenomenon seriously restricts the performance and operation safety of the compressor
Focusing on the identification of the stall inception in an axial compressor, a hybrid neural network model was proposed in this research for modeling the stability of a dynamic system
Based on the data fusion of the amplitude of the spatial mode, a hybrid neural network was adopted to represent the properties of the system by artificial intelligence learning

Summary

Introduction

As one of the most difficult problems in dynamic system stability, the rotating stall phenomenon seriously restricts the performance and operation safety of the compressor. McDougall [2] and Day [3] first discovered the pre-stall inception as the modal wave and the spike wave. Considering the importance of identifying stall precursors in advance, a lot of effort has been made in terms of signal recognition. A variance method was used by Liu [6] to detect the precursor signal of instability in the compressor. Cameron [7] summarized the application of the visual inspection method, spatial Fourier analysis and the wavelet transform method in stall initiation detection. The fast wavelet technique was employed by Liu [8] to predict the stall inception of an axial compressor. The aerodynamic system stability of compressors has been studied for years, challenges and difficulties for the initial stall detection still exist

Methods

Findings

Discussion

Conclusion