China Mechanical Engineering ›› 2021, Vol. 32 ›› Issue (17): 2090-2099,2107.

### A Novel Fault Early Warning Method for Centrifugal Blowers Based on Transfer Learning

LI Congbo1;WANG Rui1;ZHANG You1;JIANG Lijun 2;SUN Hao2

1. 1.State Key Laboratory of Mechanical Transmission，Chongqing University，Chongqing，400044
2.Chongqing General Industry（Group）Co.，Ltd.，Chongqing，401336
• Online:2021-09-10 Published:2021-09-28

### 基于迁移学习的离心鼓风机故障预警方法

1. 1.重庆大学机械传动国家重点实验室，重庆，400044
2.重庆通用工业（集团）有限责任公司，重庆，401336
• 作者简介:李聪波，男，1981年生，教授、博士研究生导师。研究方向为绿色制造、智能制造。E-mail：congboli@cqu.edu.cn。
• 基金资助:
国家自然科学基金（51975075）；
重庆市技术创新与应用示范专项（cstc2018jszx-cyzdX0146）

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.

CLC Number: