Edge-cloud collaborative transfer learning based on stacked sparse autoencoder with data regularization

Abstract

Edge-cloud collaboration provides a better solution for condition monitoring, which can reduce response time while maintaining computational efficiency. In practical condition monitoring scenarios, the individual differences among equipment often decrease the accuracy of diagnostic models. To tackle this problem, a transfer learning method based on stacked sparse autoencoder is proposed, which employs a data regularization strategy to improve feature extraction ability. The fault diagnosis model trained in the cloud transfers its model parameters and structure to the edge side. By a finetuning process with a small amount of data, and the model is further updated for condition monitoring of the individual machine. The experimental results show that the proposed KT-SAE method has improved transfer accuracy compared to other related transfer learning methods.