Abstract
In order to solve the problem that a single type of sensor cannot fully reflect the bearing life information in the process of bearing residual life prediction of metro traction motor, a bearing residual life prediction method based on multi-information fusion and convolutional neural network is proposed. Firstly, the vibration sensor and acoustic emission sensor are used to collect the bearing life signals on the bearing fatigue life test bench. Secondly, wavelet packet decomposition is used to denoise the collected bearing life signal and extract multiple eigenvalues. On this basis, the multiple eigenvalues are normalized, and the bearing degradation trend is analyzed. Finally, the collected bearing life is divided into five stages, and the processed multiple eigenvalues are fused and input into convolutional neural network for training and recognition. The results show that the probability of predicting the stage of bearing life based on multiple eigenvalues and convolutional neural network is more than 98%.
Highlights
Rolling element bearings are one of the most critical components in rotating machinery to support rotating shafts
Deng et al [6] proposed an improved MSIQDE algorithm based on hybrid Multistrategy to solve the problem that quantum differential evolution (QDE) is easy to lead to premature convergence and low search ability and fall into local optimum
In order to solve the problem that a single sensor cannot fully reflect the bearing life information, this paper uses wavelet packet decomposition [15] to denoise the collected original signal and extract multiple eigenvalues and studies the remaining life prediction of metro traction motor bearing based on information fusion and convolutional neural network
Summary
Rolling element bearings are one of the most critical components in rotating machinery to support rotating shafts. Chen [10] and others proposed a prediction model based on correlation features and multivariable support vector machine to solve the problem of using small samples to predict the residual life of rolling bearings due to the lack of sufficient condition monitoring data. Ese studies have achieved good results in the direction of bearing life prediction, but they all input a single eigenvalue into the model for training, the extracted bearing life information is limited, and the multieigenvalue information fusion technology is not considered. In order to solve the problem that a single sensor cannot fully reflect the bearing life information, this paper uses wavelet packet decomposition [15] to denoise the collected original signal and extract multiple eigenvalues and studies the remaining life prediction of metro traction motor bearing based on information fusion and convolutional neural network
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