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| 基于径向基神经网络的旋转机械故障诊断 | |
| 引用本文： | 汪庆华,王敬涛,邓东花.基于径向基神经网络的旋转机械故障诊断[J].现代电子技术,2010,33(18):141-142,150. |
| 作者姓名： | 汪庆华 王敬涛 邓东花 |
| 作者单位： | 1. 西安工业大学,机电学院,陕西,西安,710032 2. 广西石化公司机电仪中心,广西,钦州,535008 3. 中国石油天然气管道工程有限公司仪表自动化室,河北廊坊,065000 |
| 摘 要： | 针对旋转机械故障征兆与故障模式映射的复杂性,以及BP网络容易陷入局部极小、收敛速度慢等缺点,提出基于径向基（RBF）神经网络的风机故障诊断方法。以风机振动信号的7段频谱能量峰值作为故障特征,采用训练好的RBF网络进行故障辨识。结果表明,RBF网络能满足风机故障诊断的准确性,并在避免局部极小和节约训练时间方面有较好的实用性。 |
| 关 键 词： | RBF神经网络 故障诊断 风机 故障特征 |
Fault Diagnosis of Rotary Machines Based on RBF Neural Network |
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| WANG Qing-hua,WANG Jing-tao,DENG Dong-hua.Fault Diagnosis of Rotary Machines Based on RBF Neural Network[J].Modern Electronic Technique,2010,33(18):141-142,150. | |
| Authors: | WANG Qing-hua WANG Jing-tao DENG Dong-hua |
| Affiliation: | 1. School of Mechatronic Engineering, Xi＇ an Technological University, Xi＇ an 710032, China ;2. Guangxi Petrochemical Company, Qinzhou 535008, China; 3. China Petroleum Pipeline Engineering Corporation, Langfang 065000, China) |
| Abstract: | Aiming the mapping complexity between fault symptoms and fault patterns of rotary machines, and the problems of falling easily into part minimums and low velocity of convergence in BP neural networks, a fault diagnosis method of fan based on Radial Basis Function （R/3F） neural network is put forward. Making the seven frequency bands peak energy of vibration signals of a fan as fault symptoms, RBF network is trained to diagnose a fan, the results show that RBF network is a valid method of the fault diagnosis of fan in proving accuracy, repressing the network to sink local minimum and shortening the study time. |
| Keywords: | RBF neural network fault diagnosis fan fault features |
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