@inproceedings{9350df6110ea45c090980addcce85acc,
title = "Fault Diagnosis of Gearbox based on Convolutional Neural Network and Infrared Thermal Imagining",
abstract = "Diagnosis of gearbox is crucial to prevent catastrophic failure and reduce financial losses. In this study, we introduce a novel fault diagnosis technique using the infrared thermography (IRT). The IRT-based techniques have merits of non-contact measurement and high-scalability. Since the convolutional neural network (CNN) is proven to be powerful in image processing, a fault diagnosis strategy is designed by combining the IRT and CNN. Then, the pattern identification is achieved by using softmax regression (SR) classifier. One experimental data is used to validate the effectiveness of the proposed method. Results demonstrate that this diagnosis strategy can recognize gearbox with various oil-level faults. Furthermore, some important distinguishable areas of IRT images are marked for further focused research field.",
keywords = "convolutional neural networks, fault diagnosis, gearbox, infrared thermal imaging, softmax",
author = "Xiaoqiang Du and Shubin Si and Yongbo Li",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th Prognostics and System Health Management Conference, PHM-Qingdao 2019 ; Conference date: 25-10-2019 Through 27-10-2019",
year = "2019",
month = oct,
doi = "10.1109/PHM-Qingdao46334.2019.8942855",
language = "英语",
series = "2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Guo and Steven Li and Qiang Miao",
booktitle = "2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019",
}