Overlay convolutional network-based rolling bearing failure mode recognition method and device

Abstract

The invention discloses an overlay convolutional network-based rolling bearing failure mode recognition method and device, and relates to the field of rolling bearing failure diagnosis. The method comprises the following steps of: extracting a time-frequency domain feature of a vibration signal of a state-known rolling bearing; normalizing the obtained time-frequency domain feature of the state-known rolling bearing into a feature pixel according to a CNN network input format; inputting the feature pixel into a CNN network, and adjusting a model parameter of the CNN network through carrying out forward self-learning and gradient descent-based counter-propagation on the CNN network so as to obtain a trained CNN network; and during the recognition of a practical rolling bearing failure mode, extracting high-order features capable of reflecting intrinsic information layer by layer by utilizing the trained CNN network by taking a time-frequency domain feature of a vibration signal of a state-unknown rolling bearing, and inputting results of the feature self-learning into a top classifier layer by layer, so as to realize failure mode recognition of the rolling bearings under multiple working conditions and strong noises.

Description

technical field [0001] The invention relates to the field of fault diagnosis of rolling bearings, in particular to a method and device for recognizing fault patterns of rolling bearings based on a stacked convolutional network (Convolutional Neural Network, CNN). Background technique [0002] Rolling bearings are used to support the rotating shaft and the parts on the shaft of the rotating machinery of electromechanical products, and maintain the normal working position and rotation accuracy of the shaft. It is characterized by convenient use and maintenance, reliable operation, good starting performance, and high carrying capacity at medium speeds. Rolling bearings are key components commonly used in mechanical equipment, and whether their working status is normal is directly related to the normal operation status of the entire production line. Faults of rolling bearings often lead to a significant reduction in productivity, and in severe cases, even huge property losses. ...

AI Technical Summary

Problems solved by technology

[0009] The technical problem solved by the technical solution provided according to the embodiment of the present invention is that the traditional rolling bearing fault diagnosi

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