ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FOREST
Aiming at the vibration signal of rolling bearing with problems of nonlinear,small sample size and traditional machine learning based diagnosis algorithm required expert experience,a convolutional deep forest(CDF)based rolling bearing fault diagnosis algorithm was proposed.Firstly,the one-dimensiona...
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| Format: | Article |
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| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-01-01
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| Series: | Jixie qiangdu |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.06.002 |
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| collection | DOAJ |
| description | Aiming at the vibration signal of rolling bearing with problems of nonlinear,small sample size and traditional machine learning based diagnosis algorithm required expert experience,a convolutional deep forest(CDF)based rolling bearing fault diagnosis algorithm was proposed.Firstly,the one-dimensional vibration signal was preprocessed through normalization and transformation into image.Then the convolution neural network was exploited to train the image to complete the end-to-end feature extraction,and the cascade forest was used to analyze and classify the features.Finally,the effectiveness of CDF was verified on the bearing data set.The experimental results show that CDF can achieve high accuracy for small or big sample data under four loads.In addition,the accuracy of convolution neural network and CDF based on two-dimensional image are higher than one-dimensional,which proves the effectiveness of data preprocessing operation based on signal to image. |
| format | Article |
| id | doaj-art-87a0961d3f384d42aa4956b79e5ed020 |
| institution | DOAJ |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-87a0961d3f384d42aa4956b79e5ed0202025-08-20T03:11:54ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-01-01461279128698127133ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FORESTAiming at the vibration signal of rolling bearing with problems of nonlinear,small sample size and traditional machine learning based diagnosis algorithm required expert experience,a convolutional deep forest(CDF)based rolling bearing fault diagnosis algorithm was proposed.Firstly,the one-dimensional vibration signal was preprocessed through normalization and transformation into image.Then the convolution neural network was exploited to train the image to complete the end-to-end feature extraction,and the cascade forest was used to analyze and classify the features.Finally,the effectiveness of CDF was verified on the bearing data set.The experimental results show that CDF can achieve high accuracy for small or big sample data under four loads.In addition,the accuracy of convolution neural network and CDF based on two-dimensional image are higher than one-dimensional,which proves the effectiveness of data preprocessing operation based on signal to image.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.06.002 |
| spellingShingle | ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FOREST Jixie qiangdu |
| title | ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FOREST |
| title_full | ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FOREST |
| title_fullStr | ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FOREST |
| title_full_unstemmed | ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FOREST |
| title_short | ROLLING BEARING FAULT DIAGNOSIS METHOD BASED ON CONVOLUTIONAL DEEP FOREST |
| title_sort | rolling bearing fault diagnosis method based on convolutional deep forest |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.06.002 |