TY - JOUR AU - Sun, Jiahao AU - Gu, Xiwen AU - He, Jun AU - Yang, Shixi AU - Tu, Yao AU - Wu, Chenfang PY - 2022/06/07 Y2 - 2024/03/28 TI - A Robust Approach of Multi-sensor Fusion for Fault Diagnosis Using Convolution Neural Network JF - Journal of Dynamics, Monitoring and Diagnostics JA - JDMD VL - IS - SE - Regular Articles DO - 10.37965/jdmd.2022.95 UR - https://ojs.istp-press.com/dmd/article/view/95 SP - 103-110 AB - <p>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.</p><p><strong>Conflict of Interest Statement</strong><br />The authors declare no conflicts of interest.</p> ER -