Rolling bearing fault diagnosis method based on space pooling network

Abstract

The invention provides a rolling bearing fault diagnosis method based on a space pooling network. The method comprises the steps of: collecting vibration signals of a rolling bearing under a fault state and a normal state, cutting collected vibration signals of the rolling bearing to form samples, and dividing the samples into a training set, a verification set and a test set; inputting samples inthe training set and the verification set into a convolutional neural network to train the convolutional neural network and adjusting the structure of the convolutional neural network; adding spatialpooling attention after determining the last "convolution+pooling" unit of the convolutional neural network so as to achieve feature weighting, adding two spatial pooling layers and a softmax classifier, thereby completing the construction of a spatial pooling model; inputting the samples of the training set and the verification set into the space pooling network to perform parameter updating, inputting the samples of the test set into the trained space pooling network to obtain a bearing state type, comparing the bearing state type with a label, and calculating to obtain diagnosis precision.

Description

technical field [0001] The invention relates to the technical field of intelligent diagnosis of bearing faults, in particular to a method for diagnosing rolling bearing faults based on a spatial pooling network. Background technique [0002] Due to the long-term operation in the environment of heavy load and high speed, rolling bearings have become one of the most prone to failure parts in rotating machinery equipment. Therefore, early fault diagnosis of rolling bearings is of great significance. The fault diagnosis of rolling bearing is essentially a pattern recognition process. When the bearing fails, the energy of each frequency band of the vibration signal changes. In recent years, with the continuous deepening of machine learning research, great progress has been made in the use of data-driven diagnostic methods, such as support vector machines, random forests, and deep belief networks. The main idea of ​​this fault diagnosis method is to use the signal processing met...

AI Technical Summary

Problems solved by technology

This type of method does not require manual feature extraction, and can automatically learn feature extraction without requiring a large amount of prior knowledge. However, there are still defects in feature selection and training stability, and it is impossible to distinguish the hierarchy and difference of the extracted features.

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