Abstract
Compensation capacitor is an important component for extending the signal transmission of track circuit, and its safe operation is very important to the transportation business of rail transit. According to the difficulty of diagnosing the fault of compensation capacitor, a fault location model of compensation capacitor based on probabilistic neural network is established. Firstly, the influence of compensation capacitors on the current curve is analyzed from the two aspects of the failure reasons of compensation capacitors and the influence on signal transmission. Then, according to the parameters of the track circuit, the important characteristic parameters affecting the compensation capacitors are screened. According to 4 different failure modes, a fault diagnosis model based on probabilistic neural network is constructed, and the BP neural network model is selected as the comparison experiment. The results show that the compensation capacitor fault location model based on probabilistic neural network has higher relative prediction accuracy and the shortest time.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
