Prediction of Vibration Characteristics of Mechanical Bearing Based on a Novel Grey Model

• Published in: 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) pp. 143 - 146
• Main Authors: Yu, Sun, Qiang, Yuan, Rui-ping, Zhou, Xiao-fei, Wen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2019
• Subjects: Data to predict; Grey prediction theory; Manufacturing; Mechanical bearing; Mechanical bearings; Mechanical engineering; Particle swarm optimization; Particle swarm optimization (PSO); Prediction theory; Vibration signal; Vibrations
• DOI: 10.1109/WCMEIM48965.2019.00035

Shafting vibration data is one of the key parameters that can characterize shafting running state. Therefore, in this paper, vibration signals with significant bearing fault characteristic information are selected as the research object. The trend of its development is predicted. Since it is difficult to collect mechanical bearing data, the grey prediction theory which can be used to predict the bearing vibration signal development data is selected. In addition, due to the defects of the traditional grey prediction theory in the selection and calculation of parameters in the model, the paper introduces the particle swarm optimization algorithm to carry out the study on the optimization of the parameters in order to form PGM (1,1). Finally, the optimization sequence of the parameters of the grey prediction model is clarified through the example analysis and calculation. Therefore, the PGM (1,1) model proposed in this paper has a higher prediction accuracy and has certain theoretical value for the study of the service life of mechanical bearings.