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.

中文翻译：

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基于径向基函数神经网络的韦尔奇功率谱变换的滚动轴承智能故障诊断方法

在滚动轴承的智能故障诊断中，当训练样本少，噪声大时，很难达到较高的识别精度。本文提出了一种新的故障诊断方法，即基于Welch法功率谱的径向基函数神经网络。该故障诊断模型采用端到端操作方式。它以原始振动信号（时域信号）作为输入，Welch方法将数据从时域信号转换为功率谱，并抑制高强度噪声。然后用径向基函数神经网络对韦尔奇方法的结果进行分类。为了用Welch方法的功率谱测试径向基函数神经网络的性能，将该方法与一些高级故障诊断方法进行了比较，并利用Welch方法的功率谱对径向基函数神经网络进行极限性能测试，以获得其最终的诊断能力。结果表明，该方法无需从信号中提取复杂的特征即可实现较高的诊断精度。同时，在训练数据较少的情况下，该方法也可以实现高精度的诊断。此外，具有韦尔奇法功率谱的径向基函数神经网络的抗噪性能优于近年来提出的一些故障诊断方法。结果表明，该方法无需从信号中提取复杂的特征即可实现较高的诊断精度。同时，在训练数据较少的情况下，该方法也可以实现高精度的诊断。此外，具有韦尔奇法功率谱的径向基函数神经网络的抗噪性能优于近年来提出的一些故障诊断方法。结果表明，该方法无需从信号中提取复杂的特征即可实现较高的诊断精度。同时，在训练数据较少的情况下，该方法也可以实现高精度的诊断。此外，具有韦尔奇法功率谱的径向基函数神经网络的抗噪性能优于近年来提出的一些故障诊断方法。