Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CN...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/554 |
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| author | Bo Sun Wenting Hu Hao Wang Lei Wang Chengyang Deng |
| author_facet | Bo Sun Wenting Hu Hao Wang Lei Wang Chengyang Deng |
| author_sort | Bo Sun |
| collection | DOAJ |
| description | Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain. The resulting frequency domain data is then used as input to the convolutional neural network for feature extraction; Then, the weights of channel features and spatial features are assigned to the extracted features by CBAM, and the weighted features are then input into the Long Short-Term Memory (LSTM) network to learn temporal features. Finally, the effectiveness of the proposed model is verified using the PHM2012 bearing dataset. Compared to several existing RUL prediction methods, the mean squared error, mean absolute error, and root mean squared error of the proposed method in this paper are reduced by 53%, 16.87%, and 31.68%, respectively, which verifies the superiority of the method. Meanwhile, the experimental results demonstrate that the proposed method achieves good RUL prediction accuracy across various failure modes. |
| format | Article |
| id | doaj-art-8b767c7558974aceb0141a6bf775b480 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8b767c7558974aceb0141a6bf775b4802025-01-24T13:49:19ZengMDPI AGSensors1424-82202025-01-0125255410.3390/s25020554Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTMBo Sun0Wenting Hu1Hao Wang2Lei Wang3Chengyang Deng4School of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, ChinaSchool of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, ChinaSchool of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, ChinaSchool of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, ChinaSchool of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, ChinaPredicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain. The resulting frequency domain data is then used as input to the convolutional neural network for feature extraction; Then, the weights of channel features and spatial features are assigned to the extracted features by CBAM, and the weighted features are then input into the Long Short-Term Memory (LSTM) network to learn temporal features. Finally, the effectiveness of the proposed model is verified using the PHM2012 bearing dataset. Compared to several existing RUL prediction methods, the mean squared error, mean absolute error, and root mean squared error of the proposed method in this paper are reduced by 53%, 16.87%, and 31.68%, respectively, which verifies the superiority of the method. Meanwhile, the experimental results demonstrate that the proposed method achieves good RUL prediction accuracy across various failure modes.https://www.mdpi.com/1424-8220/25/2/554convolutional neural networkConvolutional Block Attention Moduledeep learningrolling bearingremaining service life prediction |
| spellingShingle | Bo Sun Wenting Hu Hao Wang Lei Wang Chengyang Deng Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM Sensors convolutional neural network Convolutional Block Attention Module deep learning rolling bearing remaining service life prediction |
| title | Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM |
| title_full | Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM |
| title_fullStr | Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM |
| title_full_unstemmed | Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM |
| title_short | Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM |
| title_sort | remaining useful life prediction of rolling bearings based on cbam cnn lstm |
| topic | convolutional neural network Convolutional Block Attention Module deep learning rolling bearing remaining service life prediction |
| url | https://www.mdpi.com/1424-8220/25/2/554 |
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