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• W3158713091 abstract "The failure of a facility to produce a product can have significant impacts on the quality of the product. Most equipment failures occur in rotating equipment, with bearing damage being the biggest cause of failure in rotating equipment. In this paper, we propose a denoising autoencoder (DAE) and multi-scale convolution recurrent neural network (MS-CRNN), wherein the DAE accurately inspects bearing defects in the same environment as bearing vibration signals in the field, and the MS-CRNN inspects and classifies defects. We experimented with adding random noise to create a dataset that resembled noisy manufacturing installations in the field. From the results of the experiment, the accuracy of the proposed method was more than 90%, proving that it is an algorithm that can be applied in the field." @default.
• W3158713091 created "2021-05-10" @default.
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• W3158713091 date "2021-04-27" @default.
• W3158713091 modified "2023-09-27" @default.
• W3158713091 title "Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments" @default.
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• W3158713091 doi "https://doi.org/10.3390/app11093963" @default.
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