Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments
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 mu...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/9/3963 |
| _version_ | 1797536024080941056 |
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| author | Seokju Oh Seugmin Han Jongpil Jeong |
| author_facet | Seokju Oh Seugmin Han Jongpil Jeong |
| author_sort | Seokju Oh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:53:42Z |
| format | Article |
| id | doaj.art-0b64fcf264484680b7e90b1e3256a12c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:53:42Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0b64fcf264484680b7e90b1e3256a12c2023-11-21T17:27:34ZengMDPI AGApplied Sciences2076-34172021-04-01119396310.3390/app11093963Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing EnvironmentsSeokju Oh0Seugmin Han1Jongpil Jeong2Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, KoreaThe 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.https://www.mdpi.com/2076-3417/11/9/3963deep learningfault detectionMS-CRNNdenoising autoencoderbearingmanufacturing |
| spellingShingle | Seokju Oh Seugmin Han Jongpil Jeong Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments Applied Sciences deep learning fault detection MS-CRNN denoising autoencoder bearing manufacturing |
| title | Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments |
| title_full | Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments |
| title_fullStr | Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments |
| title_full_unstemmed | Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments |
| title_short | Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments |
| title_sort | multi scale convolutional recurrent neural network for bearing fault detection in noisy manufacturing environments |
| topic | deep learning fault detection MS-CRNN denoising autoencoder bearing manufacturing |
| url | https://www.mdpi.com/2076-3417/11/9/3963 |
| work_keys_str_mv | AT seokjuoh multiscaleconvolutionalrecurrentneuralnetworkforbearingfaultdetectioninnoisymanufacturingenvironments AT seugminhan multiscaleconvolutionalrecurrentneuralnetworkforbearingfaultdetectioninnoisymanufacturingenvironments AT jongpiljeong multiscaleconvolutionalrecurrentneuralnetworkforbearingfaultdetectioninnoisymanufacturingenvironments |