Rolling Bearing Fault Diagnosis Based on MFDFA-SPS and ELM
Rolling bearings, as important parts on supporting rotating shafts, frequently suffer from fatigue failures. If these rolling bearing failures are not found in time, it will have a huge impact on the whole mechanical system’s operating safety and operating life. To improve the diagnosis of different...
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Published in | Mathematical problems in engineering Vol. 2022; pp. 1 - 17 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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New York
Hindawi
18.07.2022
Hindawi Limited |
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Abstract | Rolling bearings, as important parts on supporting rotating shafts, frequently suffer from fatigue failures. If these rolling bearing failures are not found in time, it will have a huge impact on the whole mechanical system’s operating safety and operating life. To improve the diagnosis of different faults as well as different degrees of faults, a fault diagnosis method based on the multifractal detrended fluctuation analysis (MFDFA) method-singularity power spectrum (SPS) with extreme learning machine (ELM) is proposed. First, MFDFA and SPS analyses are performed on vibration acceleration signals with different faults and different degrees of damage under the same operating conditions, the spectral parameters of stability and quantitative description of differentiation are selected for feature extraction, and then the selected six feature parameters are put into the extreme learning machine for fault classification. The effectiveness of the MFDFA-SPS feature extraction method is demonstrated by analyzing and testing the measured bearing signals. The fault diagnosis accuracy of the bearing fault signals can reach 99.2% based on the MFDFA-SPS with ELM method by using the Case Western Reserve database. The improvements are 6.79% and 18.42% compared to the fault diagnosis methods based on MFDFA with ELM and SPS with ELM. Compared with the methods based on MFDFA-SPS with LSSVM classifier and SVM classifier, the accuracy improvements are 3.54% and 4.25%, respectively. The results show that the method proposed in this paper can achieve the diagnosis of bearing faults and the method based on MFDFA-SPS with ELM is more efficient than the methods based on MFDFA-SPS with LSSVM and SVM classifiers, which is suitable for practical engineering problem-solving. |
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AbstractList | Rolling bearings, as important parts on supporting rotating shafts, frequently suffer from fatigue failures. If these rolling bearing failures are not found in time, it will have a huge impact on the whole mechanical system’s operating safety and operating life. To improve the diagnosis of different faults as well as different degrees of faults, a fault diagnosis method based on the multifractal detrended fluctuation analysis (MFDFA) method-singularity power spectrum (SPS) with extreme learning machine (ELM) is proposed. First, MFDFA and SPS analyses are performed on vibration acceleration signals with different faults and different degrees of damage under the same operating conditions, the spectral parameters of stability and quantitative description of differentiation are selected for feature extraction, and then the selected six feature parameters are put into the extreme learning machine for fault classification. The effectiveness of the MFDFA-SPS feature extraction method is demonstrated by analyzing and testing the measured bearing signals. The fault diagnosis accuracy of the bearing fault signals can reach 99.2% based on the MFDFA-SPS with ELM method by using the Case Western Reserve database. The improvements are 6.79% and 18.42% compared to the fault diagnosis methods based on MFDFA with ELM and SPS with ELM. Compared with the methods based on MFDFA-SPS with LSSVM classifier and SVM classifier, the accuracy improvements are 3.54% and 4.25%, respectively. The results show that the method proposed in this paper can achieve the diagnosis of bearing faults and the method based on MFDFA-SPS with ELM is more efficient than the methods based on MFDFA-SPS with LSSVM and SVM classifiers, which is suitable for practical engineering problem-solving. |
Author | Xi, Caiping Yang, Yunfan |
Author_xml | – sequence: 1 givenname: Yunfan orcidid: 0000-0002-0238-5210 surname: Yang fullname: Yang, Yunfan organization: Ocean CollegeJiangsu University of Science and TechnologyZhenjiang 212003Chinajust.edu.cn – sequence: 2 givenname: Caiping orcidid: 0000-0002-1932-6137 surname: Xi fullname: Xi, Caiping organization: School of AutomationJiangsu University of Science and TechnologyZhenjiang 212003Chinajust.edu.cn |
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Cites_doi | 10.1016/j.ymssp.2015.02.002 10.1016/j.neucom.2005.12.126 10.1016/j.physa.2015.02.025 10.1016/s0888-3270(03)00052-9 10.1137/1010093 10.1016/j.ymssp.2012.12.014 10.1126/science.156.3775.636 10.2307/2286682 10.1016/j.ymssp.2010.07.017 10.1109/tii.2022.3169465 10.1016/j.ymssp.2004.09.002 10.1109/URAI.2017.7992827 10.1155/2016/1232893 10.3389/fphys.2012.00141 10.1201/9781420006582 10.5772/20790 |
ContentType | Journal Article |
Copyright | Copyright © 2022 Yunfan Yang and Caiping Xi. Copyright © 2022 Yunfan Yang and Caiping Xi. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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Snippet | Rolling bearings, as important parts on supporting rotating shafts, frequently suffer from fatigue failures. If these rolling bearing failures are not found in... |
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SubjectTerms | Algorithms Artificial neural networks Bearings Classifiers Deep learning Fatigue failure Fault diagnosis Faults Feature extraction Fractals Machine learning Mechanical systems Methods Parameters Roller bearings Rotating shafts Signal processing Support vector machines Time series Vibration analysis |
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Title | Rolling Bearing Fault Diagnosis Based on MFDFA-SPS and ELM |
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