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

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.

[1]  Yang Ping,et al.  A method of classified HV circuit breaker fault signal based on EEMD and BP neural network , 2016, 2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT).

[2]  Luo Jian-chu.  PV short-term output forecasting based on LVQ-GA-BP neural network , 2014 .

[3]  Tiankui Zhang,et al.  An improved RBF neural network for short-term load forecast in smart grids , 2016, 2016 IEEE International Conference on Communication Systems (ICCS).

[4]  Ping Ma.  Fault Diagnosis of Rolling Bearings Based on Local and Global Preserving Embedding Algorithm , 2016 .

[5]  Zhong Xiao-fen.  Fault diagnosis of locomotive running gear rolling bearing based on PCA-LSSVM , 2014 .

[6]  Weiming Shen,et al.  A fault prediction method based on modified Genetic Algorithm using BP neural network algorithm , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[7]  Aijun Zhang,et al.  Improved CICA algorithm used for single channel compound fault diagnosis of rolling bearings , 2016 .

[8]  Qibing Jin,et al.  Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Zhang Guang,et al.  Detection of shockable rhythm using multi-parameter fusion identification and BP neural network , 2016, 2016 2nd IEEE International Conference on Computer and Communications (ICCC).

[10]  Liang Haifeng,et al.  An adaptive BP-network approach to short term load forecasting , 2004, 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies. Proceedings.

[11]  Liu Ming-deng.  Fault diagnosis of rolling bearings based on EMD and parameter adaptive support vector machine , 2013 .