A Robust Approach of Multi-sensor Fusion for Fault Diagnosis Using Convolution Neural Network
Year: 2022
Type: article
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.
Authors: Jiahao Sun, Xiwen Gu, Jun He, Shixi Yang, Yao Tu, Chenfang Wu
Institution: Zhejiang University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Awards: G2305986510, G7278324744
OpenAlex ID: W4281677181