Fault-sample-free bearing cross-working-condition fault diagnosis method

The invention discloses a fault-sample-free bearing cross-working-condition fault diagnosis method, and mainly solves the problem of low fault diagnosis precision caused by excessive dependence on prior auxiliary information in the prior art. According to the implementation scheme, a training data set and a test data set are obtained and preprocessed; constructing a health-fault category relation model composed of a sparse auto-encoder and a sparse constraint generative adversarial network, and training the health-fault category relation model by using the training data set to generate fault data missing in a target domain; constructing a fault diagnosis model which is formed by connecting a feature extraction network and a source domain classifier in series and is based on transfer learning, and training the fault diagnosis model by utilizing the training data set and the fault data; and inputting the test data set into the trained fault diagnosis model to obtain a bearing fault diagnosis result. According t