Modeling and Intelligent Identification of Axis Orbit for Rotating Machinery Based on the Convolution Neural Networks

Abstract

Based on the high dimensional complex feature recognition capability of convolutional neural networks (CNN), this paper proposes a method for orbit intelligent identification of rotating machinery based on CNN. In this method, the orbit is purified based on the frequency-domain filtering algorithm. Then an algorithm for converting the vibration signal to the orbit matrix is proposed to construct the input matrix of CNN. Finally the classification model of the CNN is established to realize the automatic identification of the orbit of rotating machines. Case studies show that the proposed method has high identification accuracy over 85% on experimental data and good universality over 73% in field data identification.