A Width Transfer Learning Network and Rolling Bearing Fault Diagnosis Method Based on Width Transfer Learning Network

Abstract

A width transfer learning network and a rolling bearing fault diagnosis method based on the width transfer learning network belong to the technical field of bearing fault diagnosis. In view of the scarcity of vibration data with marked information of rolling bearings under variable loads, the large difference in the distribution of source domain data and target domain data in the same state, the unbalanced distribution of multi-state data, and the low diagnostic accuracy and model training efficiency, a new method is proposed. Width transfer learning network and an intelligent diagnosis method for rolling bearings based on it. The present invention uses the breadth learning system to extract the characteristics of the source domain data and the target domain data and constructs a sample set, and on this basis, adopts the balanced distribution adaptation method in transfer learning to reduce the difference between the source domain and the target domain. The chicken flock algorithm is introduced to optimize the width transfer learning network parameters, and then the width transfer learning network model is established. The proposed network model is applied to the intelligent diagnosis of rolling bearing faults under variable loads, and the experimental results verify the 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...

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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 distribution of multi-state data, low diagnostic accuracy and model training efficiency, the present invention proposes a Width transfer learning network and rolling bearing fault diagnosis method based on width transfer learning network

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