采样(信号处理)
方位(导航)
断层(地质)
计算机科学
卷积神经网络
传感器融合
人工智能
模式识别(心理学)
数据挖掘
算法
计算机视觉
滤波器(信号处理)
地震学
地质学
作者
Jianbo Zheng,Chao Yang,Fangrong Zheng,Bin Jiang
标识
DOI:10.1109/icme52920.2022.9859658
摘要
In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method.
科研通智能强力驱动
Strongly Powered by AbleSci AI