The fault diagnosis of urban rail transit gearboxes has the characteristics of complex vibration signals and large amounts of data. The daily scheduled maintenance cannot meet the needs of gearbox maintenance, so it is necessary to predict the fault types in advance. In this paper, a method of gearbox fault prediction based on sparse principal component analysis and a generalized regression neural network is presented, and the result of fault prediction can provide a reference for making maintenance plans. Based on principal component analysis (PCA), a sparse principal component is obtained by adding the LASSO penalty term, which reduces the risk of overfitting of PCA while obtaining a sparse solution. Then, the sparse reduced dimension principal component is input into the generalized regression neural network model for fault diagnosis. The results show that the fault diagnosis method based on the sparse principal component-generalized regression neural network model has high accuracy and is time-consuming.
Conflicts emerge when the data transmission occurs between electrical equipment in an automobile. Hence, transmission delays appear as issues. For this reason, a method, called data transmission control, for electrical equipment of automobiles based on the 5G communication technology is proposed in this paper. Firstly, the wavelet feature decomposition was utilized to partition the frequency spectrum, and thus the statistical characteristics of electrical equipment of automobiles concerning the transmitted data were obtained. Then, the high-order approximate distribution method was adopted to construct a channel for a 5G network data transmission. Afterward, the control logic structure of the data transmission was built, and the problem, called the conflict of the data transmission, was alleviated through concurrent data collection and processing methods. On this basis, coding coefficients of a constructed global coding matrix were selected to encode and transmit source information. Also, the number of redundant data packets at each layer was adjusted. Finally, the data transmission control of the electrical equipment of the automobile was realized through the linear combination of the network nodes. The simulation results showed that the throughput of the proposed method was always higher than 7.7 MB/s, the bit error rate was around 0 when the signal-to-noise ratio was lower than 3, and the transmission delay was always below 0.5 s, which could provide a reference for the efficient and safe operation of automobiles.
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