Fault diagnosis method for rail train bearings based on multi-dimensional data space

Abstract

The invention relates to a method for diagnosing bearing faults of rail trains based on a multidimensional data space, comprising the following steps: 1) collecting sample data of normal operation of the train; 2) performing preprocessing and automatic labeling on the collected sample data, and constructing a classification model for training , to obtain classification and recognition results, and divide the data classes corresponding to the running status; 3) Construct the sample multidimensional data space by mapping the data classes with the same running status; 5) Calculate the distance between the detected sample data point and the center or centroid, and compare it with the set distance threshold, and perform error alarm. Compared with the prior art, the invention has the advantages of expanding information collection and application range, improving detection accuracy and reliability, and high diagnosis accuracy.

Description

technical field [0001] The invention relates to the field of rail train bearing fault diagnosis, in particular to a rail train bearing fault diagnosis method based on multi-dimensional data space. Background technique [0002] With the rapid development of urban rail transit in my country, rail transit is facing severe safety problems. Rail vehicles have the characteristics of complex and changeable operating states, large noise interference, complex failure modes, and high reliability requirements, which put forward higher requirements for fault diagnosis. Rolling bearings are one of the most vulnerable and important components in high-speed trains. The working environment It is complicated. When it is affected by factors such as excessive load impact or improper installation design, poor lubrication state, etc., failure may occur, resulting in major safety hazards and huge economic losses. Therefore, the fault diagnosis of train bearings can be carried out quickly and accu...

AI Technical Summary

Problems solved by technology

However, there are still certain limitations, including: the types of information collected are relatively limited, the actual operating status of the vehicle and the characteristics of different vehicles are not fully considered, there is a large amount of waste of effective information, and the lack of universal applicability; the use of a single dimension It is difficult to adapt to complex working conditions, and fall into the misunderstanding of local diagnosis. The reliability of diagnosis is insufficient, which may easily cause safety hazards; the diagnosis experience of early faults is insufficient, and the real-time diagnosis needs to be improved.

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