Binary tree filter Transform model and bearing fault diagnosis method thereof

Abstract

The invention provides a binary tree filter Transform model and a bearing fault diagnosis application thereof, a word segmentation device part in the Transform model is improved by introducing a binary tree filter, so that the improved Transform model can extract a vibration signal, and when the binary tree filter Transform model is used for bearing fault detection, the vibration signal can be extracted by using the binary tree filter Transform model, so that the bearing fault detection accuracy is improved. For an input vibration signal, a plurality of frequency bands can be effectively extracted, and the frequency bands are ranked from small to large according to frequencies, so that information under different frequency bands is effectively represented, and finally the beneficial effect of better measuring fault information is achieved. In addition, compared with a traditional convolutional neural network and a recurrent neural network, the Transform model can well give weights to different frequency bands so as to obtain a more accurate recognition rate.

Description

technical field [0001] The invention belongs to the technical field of deep learning and the technical field of mechanical fault diagnosis, and particularly relates to a binary tree filter Transformer model and a method for bearing fault diagnosis. Background technique [0002] As one of the most widely used components of rotating machinery, rolling bearings often operate under high-speed and high-load conditions. Once failure occurs, it will bring unpredictable disasters. It is the key point of bearing maintenance work to carry out fault diagnosis of bearings in time and formulate appropriate maintenance strategies, so as to effectively reduce ineffective expenses and improve maintenance efficiency. Therefore, how to carry out effective fault diagnosis of rolling bearing has important practical significance. [0003] The fault type and severity of the rolling bearing will be reflected in its vibration signal; therefore, some research works have used the vibration signal to...

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Problems solved by technology

[0005] Aiming at the deficiencies in the prior art, the present invention provides a binary tree filter Transformer model and its method for bearing fault diagnosis to solve the problem of accurate diagnosis of rolling bearing faults

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