Method and system of fault diagnosis of rail transit based on SVM (Support Vector Machine)

Abstract

The invention relates to a method and a system of fault diagnosis of a rail transit based on an SVM (Support Vector Machine). The method comprises the following steps of collecting historical monitoring data and real-time monitoring data of the rail transit and transmitting the historical monitoring data and the real-time monitoring data to a data analysis server to carry out preprocessing, feature selection, data vectoring and model training, computing, analyzing and classifying the real-time monitoring data according to a classification model which is obtained through the historical monitoring data, judging whether a fault exists and obtaining a reason for generating the fault. The system comprises a data collection component, a data storage component, a data preprocessing component, a feature selection component, a data vectoring component, a model training component and a real-time data analysis component. According to the method and the system, manual judgment and analysis of the fault in mass monitoring signals can be replaced by an automatic monitoring manner, so that a large amount of labor cost and the time for analyzing the reason of the fault can be reduced for providing a time guarantee for subsequent jobs, such as maintenance and rescue.

Description

technical field [0001] The invention provides a rail transit fault diagnosis method and system based on SVM, which relates to technical fields such as railway signal data, railway communication data, railway knowledge data, system alarm data, machine learning, SVM (support vector machine), etc., to solve Data analysis problems of rail transit monitoring data. Background technique [0002] At present, there are three main types of monitoring and maintenance products in the field of rail transit (state-owned railways, enterprise railways and urban rail transit): CSM (Centralized Signal Monitoring System), various equipment maintenance machines, and communication network management systems. In order to improve the modern maintenance level of my country's railway signal system equipment, since the 1990s, TJWX-I and TJWX-2000 have been independently developed and continuously upgraded signal centralized monitoring CSM systems. At present, most of the stations have adopted comput...

AI Technical Summary

Problems solved by technology

At present, most of them rely on manual judgment and analysis of faults in massive monitoring data, which requires a lot of labor costs and time for fault cause analysis, making it difficult to provide time guarantee for subsequent maintenance and rescue work. Therefore, it is necessary to study more efficient methods. Data Analysis and Fault Analysis Method for Rail Transit Monitoring

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