摘要
Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.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.
基金
support from the National Natural Science Foundation of China (Grant No.U1809219)
the Key Research and Development Project of Zhejiang Province (Grant No.2020C01088).