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• W2986095360 abstract "As an important component of trains, rolling bearing is always faced with the defection of shed oil, which inevitably threatens the train safety. Therefore, it is of great significance to conduct defection inspection on bearing shed oil. Due to the complex structure of rolling bearings, traditional signal analysis approaches cannot detect the defections of bearing shed oil with high-efficiency and low cost. In recent years, deep learning has achieved remarkable growth and been successfully applied to various computer-vision tasks. Motivated by this fact, we propose a two-stage attention aware method to recognize defections of bearing shed oil. The proposed method is based on convolutional neural networks, can automatically learn bearing defect features, and does not need manual feature design and extraction like traditional methods. The two-stage method cascades a bearing localization stage and a defection segmentation stage, to recognize the defect areas in a coarse-to-fine manner. The localization stage extracts the foremost bearing region and removes the useless part of images, so as to focus the attention of segmentation stage only on the target region. In segmentation stage, we propose a novel attention aware network APP-UNet16, to segment defect areas from extracted bearing region. APP-UNet16 stacks attention gates to enable the attention-aware features change adaptively, and thus can learn to focus on target defect areas automatically. We also utilize transfer learning in constructing the encoder of APP-UNet16, and introduce spatial pyramid pooling to connect the encoder and decoder, to improve traditional UNet. A series of comparative experiments are conducted, to compare our two-stage method with one-stage method which directly perform segmentation on original train images. The results indicate that the proposed two-stage inspection method achieves higher robustness and accuracy in recognizing defect areas with small oil spot. And the experimental results on proposed APP-UNet16 also demonstrate that a better segmentation performance is achieved, compared to traditional UNet and related state-of-art approaches. We will release the source code as well as the trained models to facilitate more research work." @default.
• W2986095360 created "2019-11-22" @default.
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• W2986095360 date "2020-03-01" @default.
• W2986095360 modified "2023-09-29" @default.
• W2986095360 title "A two-stage attention aware method for train bearing shed oil inspection based on convolutional neural networks" @default.
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• W2986095360 doi "https://doi.org/10.1016/j.neucom.2019.11.002" @default.
• W2986095360 hasPublicationYear "2020" @default.
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