A high-speed solenoid valve is a key component of the braking system. Accurately predicting the failure type of the solenoid valve is an important guarantee for safe operation of the braking system. However, electrical, magnetic, and mechanical coupling aging mechanism; individual differences; and uncertainty of aging processes have remained major challenges. To address this problem, a method combining physical indices and data features is proposed to predict the failure type of solenoid valve. Firstly, the mechanism model of the solenoid valve is established and five physical indices are extracted from the driven current curve. Then, the frequency band energy characteristics are obtained from the current change rate curve of the solenoid valve by wavelet packet decomposition. Combining physical indices and frequency band energy characteristics into a comprehensive feature vector, we applied random forest to both predict and classify the failure type. We generate a data set consisting of 60 high-speed solenoid valves periodically switched under accelerated aging test conditions, including driven current, final failure type, and switching cycles. The prediction result shows that the proposed method achieves 95.95% and 94.68% precision for the two failures using the driven current data of the 3000th cycle and has better prediction performance than other algorithms.
The variability of rail surfaces can result in wheel-rail slippage, which reduces the accuracy of subway braking systems, or even endangers the operation safety. It is necessary to conduct optimal anti-slip control with the estimation of the wheel-rail adhesion state. In this paper, an online super-twisting sliding mode anti-slip control strategy is proposed for subway vehicles. Firstly, real-time wheel-rail adhesion state estimation is performed by utilizing the recursive least squares algorithm under complex and variable rail surface conditions. Then, the differential evolution algorithm is adopted to search the current optimal slip velocity based on the wheel-rail adhesion state. The super-twisting sliding mode controller is designed to implement the optimal sliding velocity tracking. The controller exploits the high-order derivatives of the sliding mode to eliminate chatter vibration and avoid the effect of disturbance, improving the anti-slip control performance. Finally, the effectiveness of the proposed anti-slip strategy is verified by experimental results.
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