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Abstract The bearing voltage brings premature failure of the motor bearings in the subway traction system, which is related to the stray capacitance of the motor. Therefore, the accurate calculation of stray capacitance parameters is of great significance for predicting bearing voltage. In previous studies, stray capacitances generally were acquired from simplified model of the motor. The effect of stator end‐winding and the bearing labyrinth seal on stray capacitance was usually ignored. In this study, the stray capacitance is analysed and the finite element calculation is carried out, and the improved finite element model of motor is proposed, which includes the end‐winding treatment and the bearing labyrinth seal. Then the feasibility of the improved model is verified by the measurement, and it is found that the accurate modelling of end windings has a great influence on the capacitance, and the bearing seal capacitance cannot be ignored while it is used in the motor. Finally, the influence of labyrinth seal parameters on the bearing voltage ratio of the motor is analysed, and it is concluded that the reduction of the radial gap width or the axial gap width of labyrinth seal can effectively reduce the risk of baring current.

Poria originated from the dried sclerotium of Macrohyporia cocos is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated M. cocos, this study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of M. cocos. Therefore, we traced the origin of M. cocos from the perspectives of wild and cultivation using multiple data fusion approaches. Supervised pattern recognition techniques, like partial least squares discriminant analysis (PLS-DA) and random forest, were employed in this study using. Five types of data fusion involving low-, mid-, and high-level data fusion strategies were performed. Two feature extraction approaches including the selecting variables by a random forest-based method—Boruta algorithm and producing principal components by the dimension reduction technique of principal component analysis—were considered in data fusion. The results indicate the following: (1) The difference between wild and cultivated samples did exist in terms of the content analysis of vital chemical components and fingerprint analysis. (2) Wild samples need data fusion to realize the origin traceability, and the accuracy of the validation set was 95.24%. (3) Boruta outperformed principal component analysis (PCA) in feature extraction. (4) The mid-level Boruta PLS-DA model took full advantage of information synergy and showed the best performance. This study proved that both geographical traceability and optimal identification methods of cultivated and wild samples were different, and data fusion was a potential technique in the geographical identification.