Medical electronic equipment switching power supply fault diagnosis method based on multi-dimensional feature fusion

Abstract

The invention relates to a medical electronic equipment switching power supply fault diagnosis method based on multi-dimensional feature fusion, and belongs to the field of medical electronic equipment fault diagnosis. The method comprises the steps of acquiring electric signal data of a plurality of key test points of the medical electronic equipment switching power supply in different fault states by using a multi-channel data acquisition card; respectively extracting different features of a spatial dimension and a time dimension by utilizing a one-dimensional convolutional neural network and a bidirectional long-short-term memory network, fusing the features of different dimensions in an equal-proportion feature graph addition mode, establishing a multi-dimensional feature fusion faultdiagnosis model, and finally optimizing model parameters by using an Adam algorithm to realize intelligent drawing-free chip-level fault diagnosis of the medical electronic equipment switching power supply. According to the invention, the one-sidedness of a fault diagnosis result caused by a single feature is effectively overcome, various common fault types of the switching power supply can be accurately identified, and the accuracy is superior to that of a fault diagnosis method based on the single feature.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of medical electronic equipment, and relates to a fault diagnosis method for switching power supply of medical electronic equipment based on multi-dimensional feature fusion. Background technique [0002] Whether medical electronic equipment can exert its maximum performance is not only directly related to its own technical performance, but also has an extremely important relationship with the quality of power supply. Because the switching power supply is subjected to high voltage and strong current impact for a long time, it causes frequent failures. At the same time, the manufacturer no longer provides detailed circuit diagrams and other information in order to obtain maintenance profits, which makes the maintenance of switching power supplies difficult and expensive. At present, the method based on machine learning has been widely used in the field of fault diagnosis, but there are still some def...

AI Technical Summary

Problems solved by technology

[0005] In view of this, the purpose of the present invention is to provide a deep learning-based fault diagnosis method for switching power supplies of medical electronic equipment without drawings, which does not need to manually extract the electrical signal characteristics of switching power supplies, automatically completes end-to-end chip-level fault diagnosis, and effectively solves the problem of medical electronics. Due to frequent failures and lack of information such as drawings, the switching power supply of equipment is difficult and expensive to maintain

Images