›› 2011, Vol. 31 ›› Issue (2): 94-98.DOI: 10.3969/j.issn.1006-1355-2011.02.023
• 6.信号处理与故障诊断 • Previous Articles Next Articles
MI Jiang ,JI Guo-yi
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米江,纪国宜
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Abstract: An improved BP neural network with the methods of momentum and adaptive learning rate is applied to build the fault diagnosis system of the fans. In the process of training, standard training samples and samples with white noise are employed to train the neural network so that the neural network has some ability of fault tolerance. Results of the simulation and the fault diagnosis of a fan show that the improved BP neural network needs less training times, the learning efficiency is raised, and the phenomenon of trapping in the local minimum for the network is effectively repressed. This method is effective for the fault diagnosis of fans.
Key words: vibration and wave, fan, fault diagnosis, improved BP neural network
摘要: 采用动量法和学习速率自适应的改进BP神经网络建立风机故障诊断系统。在网络训练过程中分别采用标准训练样本和含有白噪声的训练样本来训练网络,使网络具有一定的容错性。最后通过仿真实验和风机的故障诊断实例表明:改进的BP神经网络减少训练次数,提高了学习效率,而且有效地抑制网络陷于局部极小,是风机故障诊断的有效方法。
关键词: 振动与波, 风机, 故障诊断, 改进的BP神经网络
CLC Number:
TH44
TH165+.3
MI Jiang;JI Guo-yi. Application of Improved BP Neural Network in Fault Diagnosis of Fans[J]. , 2011, 31(2): 94-98.
米江;纪国宜. 改进的BP神经网络在风机故障诊断中的应用[J]. , 2011, 31(2): 94-98.
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URL: https://nvc.sjtu.edu.cn/EN/10.3969/j.issn.1006-1355-2011.02.023
https://nvc.sjtu.edu.cn/EN/Y2011/V31/I2/94