Rail train running part rolling bearing fault diagnosis method

Abstract

The invention discloses a novel rail train running part rolling bearing fault diagnosis method. Local spectrum feature extraction is realized by providing a sliding time window segmentation algorithm based on index smoothing prediction, and fault diagnosis of a rail transit train running part bogie rolling bearing is performed through combination of an improved Adaboost algorithm. With application of the technical scheme, the nonlinear and non-stationary real-time vibration data of the rail train running part can be effectively segmented and then a local spectrum diagram is constructed, and the intermediate point frequency of the local spectrum interval is selected to act as the feature vector of the sample so that the more accurate input is provided to a classifier, the method has high accuracy in the aspect of rolling bearing fault diagnosis, the accuracy of fault classification can be effectively guaranteed, and the problems of low accuracy and difficult fault classification of the existing method can be solved.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a method for diagnosing rolling bearing faults in running parts of rail trains. The method is based on a sliding time window segmentation algorithm for exponential smoothing prediction and is used for local feature extraction. The improved Adaboost algorithm is used for fault classification. Background technique [0002] In the national economy, rolling bearings are called "the joints of industry". The development of rolling bearings often represents the development level of a country's machinery industry. In various large-scale industrial equipment, rolling bearings often operate in harsh environments such as high speed, high temperature, and high pressure, and its fault diagnosis is particularly important. Especially in the field of rail transit, its potential failure seriously threatens the safety of rail transit. The basic components of rail trains are divid...

AI Technical Summary

Problems solved by technology

Effectively guarantee the accuracy of fault classification, solving the problems of low accuracy and difficult fault classification in traditional methods

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