Rolling bearing fault identification method based on gaf-cnn-bigru network

Abstract

The invention discloses a rolling bearing fault identification method based on the GAF-CNN-BiGRU network. First, the rolling bearing vibration signal data is converted into a two-dimensional image by using the Graham angle field, and then the fault classification is completed by using the CNN-BiGRU network model; The Mu angle field converts the vibration signal data of the rolling bearing into a two-dimensional image, which not only retains the complete information of the original signal, but also preserves the dependence of the data on time; and the convolution unit in the CNN-BiGRU network model realizes the two-dimensional image The extraction of spatial features further screens out its time features through the two-way door control unit, thereby improving the accuracy of fault classification.

Description

technical field [0001] The invention belongs to the technical field of rotating machinery fault identification, relates to rolling bearing fault identification, in particular to a rolling bearing fault identification method based on a GAF-CNN-BiGRU network. Background technique [0002] Rolling bearings are the core components of rotating machinery, and their health will directly affect the performance, stability and life of rotating machinery. Studies have shown that 40% to 50% of rotating machinery failures are related to the failure of rolling bearings. In order to ensure the safety of rotating machinery operations, it is of great significance to carry out effective fault diagnosis for rolling bearings. Intelligent fault diagnosis algorithms using machine learning are widely used in the field of rolling bearing fault diagnosis. Although these methods have achieved good results, machine learning algorithms are generally shallow in structure, which limits the ability of cl...

AI Technical Summary

Problems solved by technology

However, there are still some defects in the diagnosis process: the two-dimensional images converted from vibration signals are mostly grayscale images, which contain limited feature information; Complexity; in addition, the bearing vibration signal contains time dependence, especially for vibration signals with different fault degrees, this time relationship is particularly important

However, the traditional convolutional neural network is more about extracting local spatial features of images, and it is difficult to extract such time-dependent features, which affects the final fault identification accuracy of rolling bearings.

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