Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm
Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/8471732 |
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| _version_ | 1832558198247129088 |
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| author | Qicai Zhou Hehong Shen Jiong Zhao Xingchen Liu Xiaolei Xiong |
| author_facet | Qicai Zhou Hehong Shen Jiong Zhao Xingchen Liu Xiaolei Xiong |
| author_sort | Qicai Zhou |
| collection | DOAJ |
| description | Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including RMS, kurtosis, skewness and RMSEE, and Mel-frequency cepstral coefficients features are extracted from bearing vibration signals, which are then used as the input of k-means algorithm. These unlabeled features are clustered by k-means in order to define the different categories of the bearing degradation state. In this way, the original vibration signals can be labeled. Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. So, interference brought by human factors can be eliminated, and further, the bearing degradation can be grasped so as to make maintenance plan in time. The proposed method was tested by bearing run-to-failure dataset provided by the Center for Intelligent Maintenance System, and the result proved the feasibility and reliability of the methodology. |
| format | Article |
| id | doaj-art-b6611e7f63f547ab87df0d7d2301faf7 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-b6611e7f63f547ab87df0d7d2301faf72025-02-03T01:32:57ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/84717328471732Degradation State Recognition of Rolling Bearing Based on K-Means and CNN AlgorithmQicai Zhou0Hehong Shen1Jiong Zhao2Xingchen Liu3Xiaolei Xiong4School of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaTongji Zhejiang College, Jiaxing 341000, ChinaAccurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including RMS, kurtosis, skewness and RMSEE, and Mel-frequency cepstral coefficients features are extracted from bearing vibration signals, which are then used as the input of k-means algorithm. These unlabeled features are clustered by k-means in order to define the different categories of the bearing degradation state. In this way, the original vibration signals can be labeled. Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. So, interference brought by human factors can be eliminated, and further, the bearing degradation can be grasped so as to make maintenance plan in time. The proposed method was tested by bearing run-to-failure dataset provided by the Center for Intelligent Maintenance System, and the result proved the feasibility and reliability of the methodology.http://dx.doi.org/10.1155/2019/8471732 |
| spellingShingle | Qicai Zhou Hehong Shen Jiong Zhao Xingchen Liu Xiaolei Xiong Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm Shock and Vibration |
| title | Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm |
| title_full | Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm |
| title_fullStr | Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm |
| title_full_unstemmed | Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm |
| title_short | Degradation State Recognition of Rolling Bearing Based on K-Means and CNN Algorithm |
| title_sort | degradation state recognition of rolling bearing based on k means and cnn algorithm |
| url | http://dx.doi.org/10.1155/2019/8471732 |
| work_keys_str_mv | AT qicaizhou degradationstaterecognitionofrollingbearingbasedonkmeansandcnnalgorithm AT hehongshen degradationstaterecognitionofrollingbearingbasedonkmeansandcnnalgorithm AT jiongzhao degradationstaterecognitionofrollingbearingbasedonkmeansandcnnalgorithm AT xingchenliu degradationstaterecognitionofrollingbearingbasedonkmeansandcnnalgorithm AT xiaoleixiong degradationstaterecognitionofrollingbearingbasedonkmeansandcnnalgorithm |