Width transfer learning network and rolling bearing fault diagnosis method based on same

Abstract

The invention discloses a width transfer learning network and a rolling bearing fault diagnosis method based on the same, and belongs to the technical field of bearing fault diagnosis. The invention provides a novel width transfer learning network and a rolling bearing intelligent diagnosis method based on the same, and aims to solve the problems of scarcity of vibration data with mark informationof a rolling bearing under a variable load, large distribution difference between source domain data and target domain data in the same state, unbalanced distribution of multi-state data and low diagnosis accuracy and model training efficiency. According to the invention, a width learning system is utilized to extract features of source domain data and target domain data and construct a sample set, and on the basis, a balanced distribution adaptation method in transfer learning is adopted to reduce the difference between a source domain and a target domain. A chicken swarm algorithm is introduced to optimize width transfer learning network parameters and establishing a width transfer learning network model. The proposed network model is applied to rolling bearing fault intelligent diagnosis under the variable load, and an experimental result verifies the high efficiency and accuracy of the proposed method.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method, which belongs to the technical field of bearing fault diagnosis. Background technique [0002] Rolling bearings are one of the most important components in rotating machinery, and their health status has a huge impact on the performance, stability and service life of the entire mechanical equipment [1] . In the actual working state of the rolling bearing, the load often changes, and the change of the load will directly affect the change of the vibration characteristics of the rolling bearing [2] . Therefore, under variable load conditions, it is of great significance to accurately identify the running state of the rolling bearing to ensure the normal operation of the entire mechanical equipment. [0003] In recent years, with the continuous rise of machine learning research, intelligent fault diagnosis algorithms have gained a place in the field of mechanical fault diagnosis. Litera...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the problems of scarcity of vibration data with marked information of rolling bearings under variable loads, large distribution differences between source domain data and target domain data in the same state, unbalanced

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