Railway Vehicle Fault Intelligent Maintenance System Based on Association Mining

Abstract

Aiming to improve the ability of intelligent diagnosis and maintenance of railway vehicle operation fault, the intelligent maintenance design of railway vehicle operation fault is carried out by combining the combination of fault sample sequence association and fault feature extraction. An intelligent maintenance method is proposed based on association mining for running faults of railway vehicles. The method of multi-dimensional array sensor network information acquisition is used to collect the physical data of railway rolling stock operation. The three-dimensional feature reorganization of the collected railway vehicle operation data is carried out, and the associated features reflecting the faults of mechanical and electronic systems of railway vehicles are extracted. Combined with three-dimensional atlas feature analysis technology, this paper reconstructs and analyzes the association map of railway vehicle running fault data. According to the mining result of correlation information in the atlas, the classification of railway vehicle running fault is judged. To realize the intelligent maintenance of railway vehicle running faults. The simulation results show that the accuracy of intelligent inspection and repair of railway vehicle operation faults is good, the accuracy of location and identification of railway vehicle operation failure points is high, and the stability of the system is better.