An intelligent fault diagnosis method of rolling bearings based on Welch power spectrum transformation with radial basis function neural network

Abstract

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.

A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy

“…Because of their powerful nonlinear fitting ability, RBF neural networks can map a wide range of nonlinear relationships. In contrast, most loss functions, especially in deep learning, are nonconvex, and multiple local minima can be found [ 19 ]. In addition to the function's nonconvex nature, the spatial symmetry of the neural network's weights can result in a large number of local minima, emphasizing the importance of a good optimization algorithm.…”

Technology development and commercial applications of industrial fault diagnosis system: a review

“…Network architectures applied for fault diagnosis can be separated as follows: (a) static (i.e., feed-forward) network in which the inputs for each layer only rely on the outputs of the previous layer and (b) dynamic network in which the inputs to a specific layer depend on the outputs of the previous layer and the previous iterations of the network itself [97]. Most ANN approaches proposed to date have been based on static networks, including the multi-layer perceptron (MPL) [52,[104][105][106], radial basis function network (RBF) [97,107,108], and general regression neural network (GRNN) [25]. Several dynamic networks (e.g., recurrent neural network) have been developed for fault diagnosis.…”

“…Zhou et al [107] combined unscented Kalman filter and RBF to detect fault in the pumping unit. Jin et al [108] applied radial basis function neural network with power spectrum of Welch method to bearing fault diagnosis and further discussed the limit performance of the neural network. RNN outperforms MPL and RBF due to the ability to consider temporal dependencies via local or global feedback connections in the network.…”

Wavelet and power spectrum analysis of global navigation satellite system multipath error modeling and mitigation

“…They accurately obtained the prior transmissibility functions and concluded that the proposed method performed more accurately and reliably than the conventional method. Previously, the Welch method has been used in many studies and demonstrated better results compared with nonparametric methods 75–78 . Therefore, in the current study, we applied the Welch method to detect the multipath spectrum of multiconstellation systems.…”