计算机科学
卷积神经网络
传输(电信)
人工智能
状态监测
人工神经网络
传感器融合
实时计算
频道(广播)
工程类
电信
电气工程
作者
Tianfu Li,Zhibin Zhao,Chuang Sun,Ruqiang Yan,Xuefeng Chen
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2020-08-01
卷期号:20 (15): 8364-8373
被引量:44
标识
DOI:10.1109/jsen.2020.2980596
摘要
Deep learning-based multi-sensor fusion approach has been widely used for machine condition monitoring. Complementary information from different physical sensors or the same sensors on multiple locations of the monitored target can effectively improve the accuracy of condition monitoring. While, how to distinguish importance of every single sensor for condition monitoring is rarely researched. To address this gap, an adaptive channel weighted convolutional neural network (ACW-CNN) is proposed in this paper to investigate importance of different sensors in fusion approach. The designed ACW layer in a deep neural network can calibrate sensors weight according to sensors importance. The recalibrated channel weights can be used as the guidance for sensor position optimization. Furthermore, a new loss function, that is, Focal Loss, is introduced into ACW-CNN to deal with class imbalance generated in real helicopter transmission system (HTS) monitoring. To validate the effectiveness of the proposed ACW-CNN for condition monitoring, two case studies including condition monitoring to gearbox transmission system and helicopter transmission system are carried out. The results of comparative experiments show the proposed method, that is ACW-CNN with Focal Loss, is superior to other methods in classification accuracy, G-mean, and F-measure for condition monitoring of HTS.
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