Roller Element Bearing of Mine Ventilating Fan with Fault Diagnosis Based on Mechanics Properties and RBF Neural Network

Author:

Zhang Ying Hong1,Li Cong2,Jing Hui3,Gao Bing Bing3

Affiliation:

1. Guilin University of Electronic and Technology

2. Guilin University of Aerospace Technology

3. Guilin University of Electronic Technology

Abstract

Roller element bearing is an important part of mine ventilating fan. The management and maintenance of the equipment is very important. Therefore, it is necessary to employ fault diagnosis process to the roller element bearing. In this paper, mechanics properties of roller element bearing are analyzed. Then, Radial Basis Function (RBF) neural network is used for the fault diagnosis of the roller element bearing. The structure and inference of RBF network are discussed in detail. The roller element bearing fault diagnosis model is established based on RBF network. A case study is given. The proposed method is applied to the fault diagnosis of roller element bearing. The result shows that the proposed method can improve efficiency of the fault diagnosis.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference8 articles.

1. Sitao Wu. Induction Machine Fault Detection Using SOM-Based RBF Neural Networks [J]. IEEE Transactions on Industrial Electronics, 2004, 51(1): 183-194.

2. Na Liu. Fault Diagnosis of Power Transformer Using a Combinatorial Neural Network [J]. Transactions of China Electro-technical Society, 2003, 18(2): 83-86.

3. Su Wensheng. Research on Rolling Element Bearing Vibration Signal Processing and Feature Extraction Method [D]. Doctor thesis of Dalian University of Technology. 2010 (in Chinese).

4. N.A. Tandon, A.B. Choudhury. A Review of Vibration and Acoustic Measurement Methods for Detection of Defects in Rolling Element Bearing [J]. Tribology International, 1999, 32(8): 469-480.

5. Sun Fang, Wei Zijie. Rolling Bearing Fault Diagnosis Based on Wavelet Packet and RBF Neural Network [C]. Proceedings of the 26th Chinese Control Conference, 2007: 451-455.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fault Diagnosis of Fan Bearing Based on Improved Convolution Neural Network;IOP Conference Series: Earth and Environmental Science;2021-01-01

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