An enhanced perception health state assessment method for subway sliding plug door transmission system

Abstract

The monitoring data (current and rotational speed) of the subway sliding plug door transmission system changed positively and negatively with the acceleration and weight of the door. How to perceive the changes is a challenging problem in the health state assessment of transmission system. To address this problem, an enhanced perception health state assessment method was proposed for the transmission systems. In the method, firstly, the equivalent resistance force is calculated by monitoring the current and rotational speed data according to mechanical dynamic knowledge. Secondly, the sensitive features of normal and abnormal states are screened out from the enhanced dataset constructed by current, rotational speed data, and equivalent resistance force data. Finally, the health state of the transmission system is assessed using an integrated learning algorithm. The effectiveness of the method is verified by benchmark experimental data, and the results indicate that the method has a higher accuracy with four classifiers and a broader suitable range with varying door acceleration and weight.