The Application of Radial Basis Function (RBF) Neural Network for Mechanical Fault Diagnosis of Gearbox

2017 4th International Conference on Advanced Materials, Mechanics and Structural Engineering

材料科学;机械制造

Wang, Pengbo^1

Institute of Solid Mechanics, Beihang University, Beijing University of Aeronautics and Astronautics, Beijing

100191, China^1

关键词: Basic principles;    Fast learning;    Mechanical fault diagnosis;    Mechanical faults;    Nonlinear mappings;    Radial basis function neural networks;    RBF Neural Network;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/269/1/012056/pdf
DOI  :  10.1088/1757-899X/269/1/012056

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

In this paper, the radial basis function (RBF) neural network is used for the mechanical fault diagnosis of a gearbox. We introduce the basic principles of the RBF neural network which is used for pattern classification and features a fast learning pace and strong nonlinear mapping capability; thus, it can be employed for fault diagnosis. The gearbox is a widely-used piece of equipment in engineering, and diagnosing mechanical faults is of great significance for engineers. A numerical example is presented to demonstrate the capability of the proposed method. The results indicate that the mechanical faults of a gearbox can be correctly diagnosed with a trained RBF neural network.