Department of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea.
NTT Hi-Tech Institute, Nguyen Tat Thanh University, 300A Nguyen Tat Thanh Street, Ho Chi Minh City 70000, Vietnam.
Sensors (Basel). 2021 Jan 1;21(1):244. doi: 10.3390/s21010244.
This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.
本文提出了一种新的方法，用于融合来自多个传感器系统的信息进行轴承故障诊断。在所提出的方法中，利用卷积神经网络同时处理多个信号源。本文最重要的发现是，具有宽结构的深度神经网络可以自动有效地从多个传感器信号中提取出有区别的特征。特征融合过程作为该网络的一层集成到深度神经网络中。与单个传感器情况和其他融合技术相比，所提出的方法在实际轴承数据的实验中取得了优异的性能。
