Local model migration learning-based gear fault recognition method

Abstract

The invention discloses a local model migration learning-based gear fault recognition method. The method comprises time-frequency domain feature extraction, auxiliary data set selection in migration learning and migration learning on the basis of a local model. The method comprises the following steps of: calculating a similarity between target data and auxiliary data through establishing a Wilcoxon signed rank test and chi-square test combined model on the basis of a given time-frequency domain extraction characteristic, and screening the auxiliary data; and migrating useful common parametersof the screened auxiliary data to the target data by utilizing a local migration model taking an SVM as a core so as to realize fault recognition of a gearbox. According to the method, the diagnosisprecision of machine learning when less target data exists is enhanced, the diagnosis cost is reduced, the environmental suitability and universality of gear fault diagnosis are strengthened, and potential economic value is provided.

Description

technical field [0001] This method relates to a mechanical fault diagnosis method, in particular to a gear fault identification method based on local model transfer learning. Background technique [0002] On the one hand, the current gear fault diagnosis process, especially the traditional machine learning algorithm, is often difficult to diagnose when encountering variable working conditions, variable speed and environmental conditions of the transmission gearbox. On the other hand, assuming that due to certain limitations, the amount of data collected by gears is small, in this case, those diagnostic methods that rely on more data as support will greatly reduce the accuracy of fault identification, and it is difficult to meet the diagnostic needs. The fundamental reason is that the method relies on the data learned when building the model. Once the application data deviates from the learning data, the model will no longer be applicable to new data, so it lacks versatility....

AI Technical Summary

Problems solved by technology

[0002] On the one hand, the current gear fault diagnosis process, especially the traditional machine learning algorithm, is often difficult to diagnose when encountering variable working conditions, variable speed and environmental conditions of the transmission gearbox.

On the other hand, assuming that due to certain limitations, the amount of data collected by gears is small, in this case, the fault identification accuracy of those diagnostic methods that rely on more data will be greatly reduced, and it is difficult to meet the diagnostic needs.

The fundamental reason is that the method relies on the data learned when building the model. Once the application data deviates from the learning data, the model will no longer be applicable to the new data, so it lacks versatility.

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