作者
Siyu Shao, Stephen McAleer, Ruqiang Yan, Pierre Baldi
发表日期
2019/4/1
期刊
IEEE Transactions on Industrial Informatics
卷号
15
期号
4
页码范围
2446-2455
出版商
IEEE
简介
We develop a novel deep learning framework to achieve highly accurate machine fault diagnosis using transfer learning to enable and accelerate the training of deep neural network. Compared with existing methods, the proposed method is faster to train and more accurate. First, original sensor data are converted to images by conducting a Wavelet transformation to obtain time-frequency distributions. Next, a pretrained network is used to extract lower level features. The labeled time-frequency images are then used to fine-tune the higher levels of the neural network architecture. This paper creates a machine fault diagnosis pipeline and experiments are carried out to verify the effectiveness and generalization of the pipeline on three main mechanical datasets including induction motors, gearboxes, and bearings with sizes of 6000, 9000, and 5000 time series samples, respectively. We achieve state-of-the-art results …
学术搜索中的文章
S Shao, S McAleer, R Yan, P Baldi - IEEE Transactions on Industrial Informatics, 2018