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The hunting motion of the vehicle increases the wear of the wheel tracks and affects the lateral stability of the vehicle system and safety, so the hunting motion of the vehicle needs to be recognized. However, the existing recognition methods ignore the small-amplitude hunting motion that precedes the onset of the running motion. In addition, due to the scarcity of real hunting data of high-speed trains, there is a problem of underfitting when training with traditional deep learning methods. In this paper, first, the dynamics model of a high-speed train is established, and the normal, small-amplitude hunting and hunting motions of high-speed trains in the process are simulated. Second, this paper proposes a transfer learning-based method for high-speed train hunting motion recognition. The method uses easily collected normal data samples in the training process, does not use real data samples of small-amplitude hunting and hunting motions, and completes the high-speed train running motions recognition task by transferring from simulation data (source domain) to real data (target domain). Finally, the validation is carried out using the real data, which proves that the method-related approach has some engineering application value in the intelligent monitoring of high-speed trains. [ABSTRACT FROM AUTHOR]

Electrochemical reduction reaction of carbon monoxide (CORR) offers a promising way to manufacture acetic acid directly from gaseous CO and water at mild condition. Herein, we discovered that the graphitic carbon nitride (g‐C3N4) supported Cu nanoparticles (Cu−CN) with the appropriate size showed a high acetate faradaic efficiency of 62.8 % with a partial current density of 188 mA cm−2 in CORR. In situ experimental and density functional theory calculation studies revealed that the Cu/C3N4 interface and metallic Cu surface synergistically promoted CORR into acetic acid. The generation of pivotal intermediate −*CHO is advantage around the Cu/C3N4 interface and migrated *CHO facilitates acetic acid generation on metallic Cu surface with promoted *CHO coverage. Moreover, continuous production of acetic acid aqueous solution was achieved in a porous solid electrolyte reactor, indicating the great potential of Cu−CN catalyst in the industrial application. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the non-stationary characteristics of the axle box bearing vibration acceleration signal and the background noise during the operation of the electric multiple units difficult to extract the fault characteristics, a fault diagnosis method of axlebox bearings based on average autocorrelation is proposed combined with parameter optimization variational mode decomposition (VMD). Firstly, the average autocorrelation to reduce the noise of the original signal is used to enhance the fault cycle information. Then the fault characteristic frequency energy ratio is selected as the fitness function, and Harris hawks optimization (HHO) is utilized to optimize the number of modal components and the secondary penalty coefficient of VMD to achieve the adaptive decomposition of the noise reduction signal, for extracting the best modal component. Finally, its squared envelope spectrum is calculated for fault analysis. Through simulation and test signals analyses, the results show that the method can effectively reduce the influence of background noise, stably extract the components of periodic fault impact, and realize the accurate diagnosis of axle box bearing fault. [ABSTRACT FROM AUTHOR]

Traction motor's rolling element bearings are one of the most critical components for the structural health monitoring of high-speed train vehicles. Most structural health monitoring techniques for railway rolling element bearings are using vibration signal. However, the vibration characteristic of cage defect is difficult to be transmitted to external monitoring point owing to the location of cage in bearing. Hence, the cage defect of Railway Traction Motor Bearing (RTMB) is usually difficult to be recognized with motor housing vibration acceleration signal. Considering Induction Motor (IM) itself a sensor, the mechanical imbalance of bearing cage defect can reflect in stator current through the motor gap magnetic field rather than a solid transmission path. A novel strategy with stator current for monitoring the traction motor bearing cage health status in High-Speed Train (HST) is proposed in this paper. This method requires no additional sensor in the practical application. To prove the strategy is feasible and more effective than vibration-based method for the cage fault diagnosis of RTMB, a comprehensive experiment investigation with a real railway traction motor of a HST is completed. Artificial cage defects are processed on bearings at both ends of the induction motor, respectively. Three high-speed (≥250 km/h) conditions are observed. The data analysis results show that vibration-based method is almost invalid at high-speed condition and the proposed current-based method is feasible and effective for the fault diagnosis of high-speed railway traction motor bearing cage. [ABSTRACT FROM AUTHOR]

Amorphous nano-metal catalysts often exhibit appealing catalytic properties, because the intrinsic linear scaling relationship can be broken. However, accurate control synthesis of amorphous nano-metal catalysts with desired size and morphology is a challenge. In this work, we discover that Cu(0) could be oxidized to amorphous CuxO species by supercritical CO2. The formation process of the amorphous CuxO is elucidated with the aid of machine learning. Based on this finding, a method to prepare Cu nanoparticles with an amorphous shell is proposed by supercritical CO2 treatment followed by electroreduction. The unique feature of this method is that the size of the particles with amorphous shell can be easily controlled because their size depends on that of the original crystal Cu nanoparticles. Moreover, the thickness of the amorphous shell can be easily controlled by CO2 pressure and/or treatment time. The obtained amorphous Cu shell exhibits high selectivity for C2+ products with the Faradaic efficiency of 84% and current density of 320 mA cm−2. Especially, the FE of C2+ oxygenates can reach up to 65.3 %, which is different obviously from the crystalline Cu catalysts. Amorphous metal catalysts often exhibit appealing catalytic properties, however, accurate control synthesis of amorphous nano-metal catalysts is a challenge. Here, the authors report a feasible strategy to prepare the amorphous Cu catalysts by supercritical CO2 treatment followed by electroreduction. The resulted catalyst shows high selectivity towards multi-carbon products for CO2 electroreduction. [ABSTRACT FROM AUTHOR]