Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial Networks

•A transfer learning fault diagnosis model for bearing under different working conditions is proposed.•The proposed model is a deep Fully Convolutional Conditional Wasserstein Adversarial Network (FCWAN).•A difference classifier improves the diagnosis accuracy of model. The diagnostic accuracy of existing transfer learning-based bearing fault diagnosis methods is high in the source condition, but accuracy in the target condition is not guaranteed. These methods mainly focus on the whole distribution of bearing source domain data and target condition data, ignoring the transfer learning of each kind of bearing fault data, which may lead to lower diagnostic accuracy. To overcome these limitations, we propose a transfer learning fault diagnosis model based on a deep Fully Convolutional Conditional Wasserstein Adversarial Network (FCWAN). The proposed model addresses the described problems separately: (1) A random-sampling map classification and difference classifier are used to handle the first limitation. (2) A label is introduced into the domain of adversarial learning to strengthen the supervision of the learning process and the effect of category field alignment, thus overcoming the second limitation. Experimental results demonstrate the superiority of this method over existing methods.