No Drawing Fault Diagnosis Method for Switching Power Supply Based on Feature Fusion

In order to solve the problem of fault diagnosis of switching power supply in medical equipment due to the lack of drawings, a fault diagnosis method based on feature fusion is proposed. In this paper, long short-term memory network and one-dimensional convolution neural network in deep learning are used to extract features of different dimensions and to adaptively fuse them into feature vectors. A fault intelligent diagnosis model based on feature fusion is established. At the same time, Adam algorithm is used to adaptively optimize the model to realize automatic recognition of fault state of switching power supply. Finally, taking the switch power supply of Mindray T8 monitor as an example, it is verified that the proposed method can effectively and accurately identify the common faults of the switch power supply without drawings, and the accuracy and accuracy of the model are better than the fault diagnosis method based on a single feature.