Grey Prediction of Urban Rail Transit Machine-Electric Equipment Fault Based on Data Mining

Abstract

Through the characteristic analysis of the urban rail transit machine-electric equipment fault alarm, mining the type, frequency and multi-source heterogeneous characteristics of fault data, the paper forms the corresponding data system and knowledge discovery while it builds the mathematical modeling of machine electric equipment fault prediction based on grey theory, carries on the design and implementation of the corresponding system and algorithm. Besides, the paper does a city track traffic machine-electric equipment alarm data as a case which application results show that it can analyze the characteristic of machine-electric equipment fault data correctly and formats the early warning information auxiliary operation maintenance and overhaul.

Analysis Platform of Rail Transit Vehicle Signal System Based on Data Mining

“…The observed pedestrians need to wear hats of different colors. Using the hats as recognition conditions, the pattern recognition of the captured video images can be used to obtain the motion trajectories of ordinary behaviors and specific behaviors [18]. Chen et al propagandized the pedestrian detection and tracking technology based on Blob analysis, and developed a passenger micro-traffic behavior parameter acquisition system (Ped Trace) to extract traffic behavior characteristic data such as trajectory, passenger speed, pedestrian distance, and acceleration [19].…”