Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

Xinfeng Ge,Jinqi Du,Ye Zhou,Jianguo Cai,Yuan Zheng,Ming Zhao,Jing Zhang,Hui Zhang,Dong Chen,Hongchang Hua,Ling Zhou

doi:10.1155/2021/6680640

Abstract

In the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. Shaft orbit identification has important significance for vibration fault diagnosis. In getting the feature extraction and pattern recognition of a shaft orbit, the Zernike moment is better than the Hu moment; it has the advantages of a small calculation error and a high recognition rate. A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. The simulation of the recognition of shaft orbits shows that the hybrid model has achieved good results in the identification of shaft orbits.

Highlights

Because of the complex operating conditions involved, there are many factors that cause the instability of hydropower units, including hydraulic, mechanical, and electromagnetic factors
Fourier descriptors have the advantage of feature extraction for completely closed curves, but the shaft orbits’ coincidence performance for the hydropower unit is so poor that it is hardly closed, so this paper uses Hu moments and Zernike moments to extract features of the shaft orbit
In order to describe the vibration mode of the hydrogenerator set as much as possible, more units are set up to extract the characteristic quantities of these units, some of which are related and some of which are independent. ese feature parameters are often incomplete and redundant, which makes the modeling of subsequent fault diagnosis complex. e rough set theory can be used to reduce the knowledge of the training samples of neural network and get fewer attribute values while ensuring the goal of not missing important information

Summary

Introduction

Because of the complex operating conditions involved, there are many factors that cause the instability of hydropower units, including hydraulic, mechanical, and electromagnetic factors. E feature extraction methods include Fourier descriptors [8,9,10,11], Hu moments [12,13,14,15], and Zernike moments [16], as well as classification of the data with an advanced pattern recognition method [17]. Fourier descriptors have the advantage of feature extraction for completely closed curves, but the shaft orbits’ coincidence performance for the hydropower unit is so poor that it is hardly closed, so this paper uses Hu moments and Zernike moments to extract features of the shaft orbit. This paper adopts the common BP neural network and applies the rough set theory to simplify the neural network with overly large input samples and a complex network structure

Formation and Characteristics of the Shaft Orbit

Hu Moments and Improvement

Two separate ellipses

Simulation and Pattern Recognition of the Shaft Orbit

Conclusions