Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy
An improved density-based spatial clustering of applications with noise (IDBSCAN) analysis approach based on kurtosis and sample entropy (SE) is presented for the identification of operational state in order to provide accurate monitoring of spindle operation condition. This is because of the low st...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/5/363 |
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| author | Yanfei Zhang Yunhao Li Lingfei Kong Qingbo Niu Yu Bai |
| author_facet | Yanfei Zhang Yunhao Li Lingfei Kong Qingbo Niu Yu Bai |
| author_sort | Yanfei Zhang |
| collection | DOAJ |
| description | An improved density-based spatial clustering of applications with noise (IDBSCAN) analysis approach based on kurtosis and sample entropy (SE) is presented for the identification of operational state in order to provide accurate monitoring of spindle operation condition. This is because of the low strength of the shock signal created by bearing of precision spindle of misalignment or imbalanced load, and the difficulties in extracting shock features. Wavelet noise reduction begins by dividing the recorded vibration data into equal lengths. Features like kurtosis and entropy in the frequency domain are used to generate feature vectors that indicate the bearing operation state. IDBSCAN cluster analysis is then utilized to establish the ideal neighborhood radius (<i>Eps</i>) and the minimum number of objects contained within the neighborhood radius (<i>MinPts</i>) of the vector set, which are combined to identify the bearing operating condition features. Finally, utilizing data from the University of Cincinnati, the approach was validated and assessed, attaining a condition detection accuracy of 99.2%. As a follow-up, the spindle’s vibration characteristics were studied utilizing an unbalanced bearing’s load bench. Bearing state recognition accuracy was 98.4%, 98.4%, and 96.7%, respectively, under mild, medium, and overload circumstances, according to the results of the experimental investigation. Moreover, it shows that conditions of bearings under various unbalanced loads can be precisely monitored using the proposed method without picking up on specific sorts of failures. |
| first_indexed | 2024-03-10T03:33:11Z |
| format | Article |
| id | doaj.art-c6534c9703484a1ca95b9a5f40245426 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T03:33:11Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-c6534c9703484a1ca95b9a5f402454262023-11-23T11:52:53ZengMDPI AGMachines2075-17022022-05-0110536310.3390/machines10050363Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample EntropyYanfei Zhang0Yunhao Li1Lingfei Kong2Qingbo Niu3Yu Bai4School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaLuoyang Bearing Science & Technology Co., Ltd., Luoyang 471039, ChinaAviation Industry Corporation of China Co., Ltd., Xi’an 710089, ChinaAn improved density-based spatial clustering of applications with noise (IDBSCAN) analysis approach based on kurtosis and sample entropy (SE) is presented for the identification of operational state in order to provide accurate monitoring of spindle operation condition. This is because of the low strength of the shock signal created by bearing of precision spindle of misalignment or imbalanced load, and the difficulties in extracting shock features. Wavelet noise reduction begins by dividing the recorded vibration data into equal lengths. Features like kurtosis and entropy in the frequency domain are used to generate feature vectors that indicate the bearing operation state. IDBSCAN cluster analysis is then utilized to establish the ideal neighborhood radius (<i>Eps</i>) and the minimum number of objects contained within the neighborhood radius (<i>MinPts</i>) of the vector set, which are combined to identify the bearing operating condition features. Finally, utilizing data from the University of Cincinnati, the approach was validated and assessed, attaining a condition detection accuracy of 99.2%. As a follow-up, the spindle’s vibration characteristics were studied utilizing an unbalanced bearing’s load bench. Bearing state recognition accuracy was 98.4%, 98.4%, and 96.7%, respectively, under mild, medium, and overload circumstances, according to the results of the experimental investigation. Moreover, it shows that conditions of bearings under various unbalanced loads can be precisely monitored using the proposed method without picking up on specific sorts of failures.https://www.mdpi.com/2075-1702/10/5/363spindle bearingunbalanced loadfrequency domain sample entropyIDBSCANcondition monitoring |
| spellingShingle | Yanfei Zhang Yunhao Li Lingfei Kong Qingbo Niu Yu Bai Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy Machines spindle bearing unbalanced load frequency domain sample entropy IDBSCAN condition monitoring |
| title | Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy |
| title_full | Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy |
| title_fullStr | Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy |
| title_full_unstemmed | Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy |
| title_short | Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy |
| title_sort | improved dbscan spindle bearing condition monitoring method based on kurtosis and sample entropy |
| topic | spindle bearing unbalanced load frequency domain sample entropy IDBSCAN condition monitoring |
| url | https://www.mdpi.com/2075-1702/10/5/363 |
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