Train fault diagnosis system and method based on vehicle and cloud

Abstract

The invention provides a train fault diagnosis system and method based on a vehicle and cloud, and relates to the technical field of high-speed rail large data fault diagnosis. The system comprises a vehicle fault diagnosis subsystem and a cloud fault diagnosis subsystem. The vehicle fault diagnosis subsystem is combined with the cloud fault diagnosis subsystem. Cloud service is used to monitor the running process of a train. Cloud data are continuously updated, and a fault diagnosis and prediction model is continuously optimized and updated, and can visualize a diagnosis process. According to the invention, the details of the fault diagnosis and prediction model are more and more perfect; the precision is improved continuously; the performance is beyond the control of a traditional single real-time fault diagnosis module; the requirements of high reliability and high precision of the train fault diagnosis subsystem in the running process are satisfied; and the safety of the train in the running process is improved.

Description

technical field [0001] The invention relates to the technical field of high-speed rail big data fault diagnosis, in particular to a vehicle-mounted and cloud-based train fault diagnosis system and method. Background technique [0002] At present, my country's high-speed railway network is developing rapidly, which has a positive effect on its economic efficiency as soon as possible. Due to the fast running speed of the train, in order to better grasp the running state of the train and diagnose the train fault in time, a highly reliable train fault diagnosis control system is required. The train state monitoring and fault diagnosis system is an important part of the railway safety guarantee system. [0003] Existing train fault diagnosis mainly uses on-board system control, adopts the traditional method based on diagnostic rules, detects the deviation of expected behavior on the basis of available state variables, and diagnoses the fault location with the help of knowledge ab...

AI Technical Summary

Problems solved by technology

This method still has many shortcomings: 1. The fault diagnosis is only based on the on-board system, and the diagnosis efficiency is low; 2. The model algorithm is single, and the calculation model cannot be improved and optimized according to the train operation situation, and the diagnosis reliability of the train on-board fault diagnosis system is low.

[0005] To sum up, the traditional train fault diagnosis is only based on the on-board system. Due to the rapid growth of train data and the limited computing power and hardware resources of the on-board system, it is difficult for high-speed trains to complete the large-scale and rapidly accumulated large-scale data during the running process. analytical modeling

At the same time, in view of the special space environment of high-speed trains, it is impossible to deploy large-scale computing clusters on the trains to perform large-scale high-speed calculations to establish high-precision train fault diagnosis system models in real time, and the calculation models cannot be improved and optimized according to the train operating conditions, resulting in The fault diagnosis rate of the train on-board fault diagnosis subsystem is low, which cannot meet the high-precision and high-reliability monitoring requirements of high-speed trains under complex conditions

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