High-speed train rolling bearing fault diagnosis method

Abstract

A high-speed train rolling bearing fault diagnosis method includes the steps: acquiring original vibration signals and decomposing the original vibration signals by an EEMD (ensemble empirical mode decomposition) method, selecting previous a IMF (interactive media forum) components, calculating energy of the components and total energy and normalizing the components to obtain an energy feature vector; determining an RBF (radial basis function) nerve network structure, determining the node number of an input layer, an output layer and a hidden layer, determining training target precision and distribution density, selecting a training sample and a testing sample, taking the training sample as input for training, acquiring a preliminary RBF nerve network diagnosis model after reaching target precision, taking the testing sample as input of a preliminary model to recognize the testing sample, and acquiring a final RBF nerve network diagnosis model for diagnosing a bearing fault type if a fault recognition rate meets an ideal standard. A new idea is provided for improving high-speed train rolling bearing fault diagnosis accuracy and instantaneity, and the performance and running safety of a high-speed train are further ensured.

Description

technical field [0001] The invention relates to a fault diagnosis method for modeling and classification using characteristic signals, in particular to a high-speed train rolling bearing fault diagnosis method based on EEMD and RBF neural network. Background technique [0002] Rolling bearings are one of the important components of high-speed trains, and their condition is crucial to the safe operation of trains. Speeding up and increasing load is the development trend of railways all over the world, and having trains with full traction is the premise of increasing speed and capacity. At this time, rolling bearings, one of the important components of high-speed trains, deserve more attention. As a mechanical wearing part, a rolling bearing has a remarkable feature that its life span is large and its failure causes are complicated. In practical applications, some rolling bearings have been used for a long time before reaching the design life, but there are various failures, ...

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a high-speed train rolling bearing fault diagnosis method for the shortcomings of the prior art that the speed of building models is not fast and the accuracy of fault identification is not high

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