Polynomial kernel implanting feature distribution adaptation-based rolling bearing fault migration diagnosis method

Abstract

The invention relates to a polynomial kernel implanting feature distribution adaptation-based rolling bearing fault migration diagnosis method. According to the method, a health mark-containing data set of a source domain rolling bearing and the monitoring data set of a target domain rolling bearing are acquired, and are inputted into a deep residual network, thereafter, source domain and target domain migration fault features are extracted layer by layer; distribution difference is minimized according to polynomial kernel implanting feature adaptation; the target domain fault features are made to pass through a softmax classifier, so that the probability distribution of the specific health state of a target domain sample can be obtained, and the probability distribution is converted into the pseudo-mark of the target domain sample; and after a migration diagnosis model is trained through the obtained distribution difference and the target domain pseudo-mark, the monitoring data of the target domain bearing are inputted into the trained diagnosis model, label probability distribution corresponding to the data sample is outputted, and a sample label corresponding to the maximum probability is the health state of the rolling bearing. With the polynomial kernel implanting feature distribution adaptation-based rolling bearing fault migration diagnosis method of the invention adopted, the performance and training efficiency of the migration diagnosis model are improved, and parameter adjustment difficulty is reduced.

Description

technical field [0001] The invention belongs to the technical field of rolling bearing fault diagnosis, and in particular relates to a rolling bearing fault migration diagnosis method adapted to feature distribution of polynomial kernel implantation. Background technique [0002] As one of the important components of rotating machinery, rolling bearings often directly affect the overall performance of mechanical equipment, so the fault diagnosis of rolling bearings has practical engineering significance. Intelligent fault diagnosis, because it can automatically extract fault features and identify the health status of components, has become a research hotspot in fault diagnosis technology. With the rapid rise and development of deep learning theory and technology, and gradually applied to the intelligent diagnosis of bearing faults, the accuracy and intelligence level of its diagnosis have been significantly improved. The performance reliability of in-depth intelligent diagn...

AI Technical Summary

Problems solved by technology

At present, the feature distribution adaptation method generally adopts the maximum mean difference adaptation method based on Gaussian kernel implantation. However, this method has the following significant disadvantages: ① The feature distribution adaptation of Gaussian kernel implantation only considers , that is, the distribution difference on the mean value, ignoring the distribution difference on the high-order moments, resulting in inaccurate measurement of the characteristic distribution difference and low diagnostic accuracy of the migration diagnosis of the diagnostic model; ②The computational complexity of the characteristic distribution adaptation of Gaussian kernel implantation is too high High, the calculation cost is huge (long time-consuming), which makes the training of the migration diagnosis model more difficult; ③The feature distribution adaptation of Gaussian kernel implantation is very sensitive to the change of kernel parameters, resulting in unstable output changes and difficult adjustment of model parameters

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