Temperature prediction of multi - Factor rolling bearings based on RBF neural network

• Published in: 2017 Chinese Automation Congress (CAC) pp. 425 - 429
• Main Authors: Jun Li, Jiangwen Xiao, Yuling Hu, Kun Chen, Ying Zou, Yiwen Xiao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2017
• Subjects: Automation; BP neural network; Fault diagnosis; Neural networks; RBF neural network; rolling bearing; Rolling bearings; Temperature; Temperature measurement; temperature prediction; Training
• DOI: 10.1109/CAC.2017.8242805

In this paper, a multi - factor prediction model based on Radical Basis Function(RBF) neural network is proposed to accurately predict the temperature of rolling bearing. According to the factors that affect the rolling bearing, including load, speed, vibration, displacement, bearing temperature and ambient temperature, the working temperature of the rolling bearing is predicted by combining the historical data and real-time data of these factors. The research object is 1 #location rolling bearing of a water pump system of Shanghai JiaChuang precision machine Co., Ltd. Based on the historical data of the research object, the results show that it can achieve higher precise temperature prediction of rolling bearings through RBF neural network for the temperature prediction than BP neural network algorithm under the same conditions.