径向基函数
人工神经网络
断层(地质)
径向基函数网络
噪音(视频)
时域
光谱密度
模式识别(心理学)
信号(编程语言)
计算机科学
基础(线性代数)
试验数据
频域
功率(物理)
转化(遗传学)
人工智能
控制理论(社会学)
数学
计算机视觉
电信
几何学
控制(管理)
程序设计语言
化学
地震学
地质学
物理
图像(数学)
基因
生物化学
量子力学
作者
Zhihao Jin,Qicheng Han,Kai Zhang,Yimin Zhang
标识
DOI:10.1177/1077546319889859
摘要
In the intelligent fault diagnosis of rolling bearings, the high recognition accuracy is hardly achieved when small training samples and strong noise happen. In this article, a novel fault diagnosis method is proposed, that is radial basis function neural network with power spectrum of Welch method. This fault diagnosis model adopts the way of end-to-end operating mode. It takes the original vibration signal (time-domain signal) as input, and Welch method transforms the data from time-domain signals to power spectrums and suppresses high strength noise. Then the results of Welch method are classified by radial basis function neural network. To test the performance of radial basis function neural network with power spectrum of Welch method, the method is compared with some advanced fault diagnosis methods, and the limit performance test for radial basis function neural network with power spectrum of Welch method is carried out to obtain its ultimate diagnosis ability. The results show that the proposed method can realize the high diagnostic precision without the complex feature extraction from the signal. At the same time, in the case of a small amount of training data, this method also can achieve the diagnosis in high precision. Moreover, the anti-noise performance of radial basis function neural network with power spectrum of Welch method is better than the performance of some fault diagnosis methods proposed in recent years.
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