Data processing method and device for train set failure prediction

Abstract

The invention provides a data processing method for train set failure prediction. The method includes the steps of sending a request for failure prediction required data to a data preprocessing module, and receiving the requested data from the data preprocessing module for train set failure prediction, wherein the data obtained from service systems of a train set is classified to be stored in thedata preprocessing module according to a preset data model. The invention further provides a data processing device for train set failure prediction. Multi-source heterogeneous data related to the train set failure prediction are unified and classified to be stored, network resources are saved, data response speed is increased, the failure prediction speed and accuracy are improved, and the decision making capacity of a rail system for operation and maintenance activities is improved.

Description

technical field [0001] The invention relates to the field of railway vehicle failure prediction, in particular to a data processing method and device for train group failure prediction. Background technique [0002] Fault prediction refers to carrying out predictive diagnosis and fault trend analysis based on the existing or historical performance status and data information of the equipment or system to determine the degree of mechanical wear, remaining life or normal working time of the equipment or system. Take maintenance measures at an appropriate time before the monitored equipment or system fails. Fault prediction can improve the decision-making ability of the railway system for maintenance activities, and the maintenance and management of key components of the train set is an important factor to ensure the safety of all links during transportation. [0003] The fault prediction of the key components of the train set involves the data of many information systems in t...

AI Technical Summary

Problems solved by technology

These data are stored in different locations and in different business systems. In the prior art, in order to predict trainset failures, each collection of data needs to be accessed, processed, and analyzed for each related system. The collection and processing process is cumbersome and cumbersome. It takes a long time, resulting in low efficiency and accuracy of fault prediction

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