A Robust Approach of Multi-sensor Fusion for Fault Diagnosis Using Convolution Neural Network

Jiahao Sun; Xiwen Gu; Jun He; Shixi Yang; Yao Tu; Chenfang Wu

doi:10.37965/jdmd.2022.95

Authors

Jiahao Sun State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University,China https://orcid.org/0000-0002-0930-2692
Xiwen Gu State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University,China
Jun He State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University,China
Shixi Yang State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University,China
Yao Tu Hangzhou Steam Turbine & Power Group Co., Ltd, China
Chenfang Wu State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University,China

DOI:

https://doi.org/10.37965/jdmd.2022.95

Keywords:

multi-sensor fusion, fault diagnosis, deep learning, engineering application

Abstract

Multi-sensor measurement is widely employed in rotating machinery to ensure the safety of machines. The information provided by the single sensor is not comprehensive. Multi-sensor signals can provide complementary information in characterizing the health condition of machines. This paper proposed a multi-sensor fusion convolution neural network (MF-CNN) model. The proposed model adds a 2-D convolution layer before the classical 1-D CNN to automatically extract complementary features of multi-sensor signals and minimize the loss of information. A series of experiments are carried out on a rolling bearing test rig to verify the model. Vibration and sound signals are fused to achieve higher classification accuracy than typical machine learning model. In addition, the model is further applied to gas turbine abnormal detection, and shows great robustness and generalization.

Conflict of Interest Statement
The authors declare no conflicts of interest.