Evaluation of Rolling Bearing Performance Degradation Using Wavelet Packet Energy Entropy and RBF Neural Network

Abstract

Rolling bearings are the most important parts in rotating machinery, and one of the most vulnerable parts to failure. The rolling bearing is a cyclic symmetrical structure that is stable under normal operating conditions. However, when the rolling bearing fails, its symmetry is destroyed, resulting in unstable performance and causing major accidents. If the performance of rolling bearings can be monitored and evaluated in real time, maintenance strategies can be implemented promptly. In this paper, by using wavelet packet energy entropy (WPEE), the early fault-free features of bearing and the failure samples of similar bearings are decomposed firstly, and the energy value is extracted as the original feature, simultaneously. Secondly, a radial basis function (RBF) neural network model is established by using early fault-free features and similar bearing failure characteristics. The bearing full-life data characteristics of the extracted features are added into the RBF model in an iterative manner to obtain performance degradation Indicator. Boxplot was introduced as an adaptive threshold method to determine the failure threshold. Finally, the results are verified by empirical mode decomposition and Hilbert envelope demodulation. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method. The experimental results show that the method can diagnose early fault points in time and evaluate the degree of bearing degradation, which is of great significance for industrial practical applications.

Online Incipient Fault Detection Method Based on Improved ℓ1 Trend Filtering and Support Vector Data Description

“…Using the method of combining the hierarchical Dirichlet process and the continuous hidden Markov model, Wang et al [49] identified the incipient fault occurrence at the No.576 data file, which is 440 minutes later than the method proposed in this paper. Using wavelet packet energy entropy and RBF neural network, Zhou et al [13] detected the incipient fault in No.533 data file, which is 10 minutes later than the method proposed in this paper. As shown in Fig.…”

“…Rai et al [12] used a method based on the empirical mode decomposition and kmedoids clustering. Zhou et al [13] utilized wavelet packet energy entropy and radial basis function neural network to detect the time of the incipient bearing failure. Mao et al [14] proposed a semi-supervised architecture and depth feature representation method for bearing online IFD, which was based on stacked denoising auto-encoder and semi-supervised support vector machine.…”

Stages prediction of the remaining useful life of rolling bearing based on regularized extreme learning machine

“…The wavelet packet is chosen because it has a better decomposition effect on non-stationary signals. 30 And through literature research, it is found that wavelet packet transform can effectively enhance the fault and degradation characteristics. 31 , 32…”

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“…The boxplot, also known as a box-whisker plot, was invented by John Tukey, a famous American statistician, in 1977. It can accurately and reliably describe the discrete distribution of data and can quickly identify outliers [48]. The structure of a boxplot is shown in Figure 8.…”