Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN
Remaining useful life prediction of a milling tool is one of the determinants in making scientific maintenance decision for the CNC machine tool. Predicting the RUL accurately can improve machining efficiency and the quality of product. Deep learning methods have strong learning capability in RUL pr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2023/1830694 |
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| _version_ | 1832551136155926528 |
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| author | Ning Hu Zhenguo Liu Shixin Jiang Quanzhou Li Shuqi Zhong Bingquan Chen |
| author_facet | Ning Hu Zhenguo Liu Shixin Jiang Quanzhou Li Shuqi Zhong Bingquan Chen |
| author_sort | Ning Hu |
| collection | DOAJ |
| description | Remaining useful life prediction of a milling tool is one of the determinants in making scientific maintenance decision for the CNC machine tool. Predicting the RUL accurately can improve machining efficiency and the quality of product. Deep learning methods have strong learning capability in RUL prediction and are extensively used. Multiscale CNN, a typical deep learning model in RUL prediction, has a large number of parameters because of its parallel convolutional pathways, resulting in high computing cost. Besides, the MSCNN ignores various influences of different scales of degradation features on RUL prediction accuracy. To address the issue, a pyramid CNN (PCNN) is proposed for RUL prediction of the milling tool in this paper. Group convolution is used to replace parallel convolutional pathways to extract multiscale features without additional large number of parameters. And the channel attention with soft assignment is used to select the key degradation features, considering different sensors and scales. The milling tool wear experiments show that the score value of the proposed method achieved 51.248 ± 1.712 and the RMSE achieved 19.051 ± 0.804, confirming better performance of the proposed method compared with the traditional MSCNN and other deep learning methods. Besides, the number of parameters of the proposed method is reduced by 62.6% and 54.8% compared with the MSCNN with self-attention and the MSCNN methods, confirming its lower computing cost. |
| format | Article |
| id | doaj-art-ad650cd19851434f9e5c49c6212c4e15 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-ad650cd19851434f9e5c49c6212c4e152025-02-03T06:04:52ZengWileyShock and Vibration1875-92032023-01-01202310.1155/2023/1830694Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNNNing Hu0Zhenguo Liu1Shixin Jiang2Quanzhou Li3Shuqi Zhong4Bingquan Chen5China Electronic Product Reliability and Environmental Test InstituteChina Electronic Product Reliability and Environmental Test InstituteChina Electronic Product Reliability and Environmental Test InstituteChina Electronic Product Reliability and Environmental Test InstituteChina Electronic Product Reliability and Environmental Test InstituteChina Electronic Product Reliability and Environmental Test InstituteRemaining useful life prediction of a milling tool is one of the determinants in making scientific maintenance decision for the CNC machine tool. Predicting the RUL accurately can improve machining efficiency and the quality of product. Deep learning methods have strong learning capability in RUL prediction and are extensively used. Multiscale CNN, a typical deep learning model in RUL prediction, has a large number of parameters because of its parallel convolutional pathways, resulting in high computing cost. Besides, the MSCNN ignores various influences of different scales of degradation features on RUL prediction accuracy. To address the issue, a pyramid CNN (PCNN) is proposed for RUL prediction of the milling tool in this paper. Group convolution is used to replace parallel convolutional pathways to extract multiscale features without additional large number of parameters. And the channel attention with soft assignment is used to select the key degradation features, considering different sensors and scales. The milling tool wear experiments show that the score value of the proposed method achieved 51.248 ± 1.712 and the RMSE achieved 19.051 ± 0.804, confirming better performance of the proposed method compared with the traditional MSCNN and other deep learning methods. Besides, the number of parameters of the proposed method is reduced by 62.6% and 54.8% compared with the MSCNN with self-attention and the MSCNN methods, confirming its lower computing cost.http://dx.doi.org/10.1155/2023/1830694 |
| spellingShingle | Ning Hu Zhenguo Liu Shixin Jiang Quanzhou Li Shuqi Zhong Bingquan Chen Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN Shock and Vibration |
| title | Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN |
| title_full | Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN |
| title_fullStr | Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN |
| title_full_unstemmed | Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN |
| title_short | Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN |
| title_sort | remaining useful life prediction of milling tool based on pyramid cnn |
| url | http://dx.doi.org/10.1155/2023/1830694 |
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