关键词: 故障预警, 离心鼓风机, 自编码, 迁移学习

Abstract: The fault early warning model for centrifugal blowers established by laboratory data become invalid when used in factories. And data collecting there was difficult to build high-precision model. To address this problem， this paper proposed a transfer learning method based on AE， which had ability to build the fault early warning model suitable for factory environments quickly. Firstly， the monitoring data of centrifugal blowers collected in the laboratory was windowed resampled to establish an AE model with sparse constraints. Secondly， the factory and laboratory data were input into the AE network to obtain low-dimensional features， and  the maximum mean discrepancy（MMD）between the two low-dimensional features was minimized， then  the AE model with small learning rate was fine-tuned to complete the transfer of AE model. Finally， based on the trained AE model and early warning in dicator， the fault early warning strategy was formulated to realize fault early warning for centrifugal blowers under the actual environments of the factories. The experimental results on a centrifugal blower show that the proposed method has higher accuracy compared with other three methods. 

Key words: fault early warning, centrifugal blower, autoencoder(AE), transfer learning