The invention discloses a three-phase
inverter open-circuit fault diagnosis method based on deep transfer learning. The three-phase
inverter open-circuit fault diagnosis method comprises the following steps: step 100, collecting a sample set 1 with a
label in a source domain; collecting a sample set 2 with labels in the target domain and a sample set 3 without labels in the target domain; step 200, constructing a
convolutional neural network model for fault diagnosis, and training the model by using a sample set 1 of a source domain to obtain a pre-training model alpha; step 300, constructing a fault diagnosis model based on transfer learning on the basis of the pre-training model alpha, and training the model by using a sample set 2 of a target domain to obtain a
transfer model beta; step 400, using the migration model beta to predict unlabeled samples in the unlabeled sample set 3 to generate a pseudo
label, and using a consistency regularization technology to smooth a pseudo
label prediction result for subsequent training of the migration model beta to obtain a final optimization model gamma; and step 500, inputting to-be-detected three-phase
inverter data into the model gamma, and outputting a predicted health state. Through the method, the problem of insufficient target domain samples is solved, and the diagnosis accuracy is improved.