Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier
Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based cla...
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| Autor*in: |
Gundewar, Swapnil K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Systems Assurance Engineering and Management - Springer-Verlag, 2010, 13(2022), 6 vom: 24. Aug., Seite 2876-2894 |
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| Übergeordnetes Werk: |
volume:13 ; year:2022 ; number:6 ; day:24 ; month:08 ; pages:2876-2894 |
| Links: |
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| DOI / URN: |
10.1007/s13198-022-01757-4 |
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SPR048811629 |
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| 520 | |a Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. | ||
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10.1007/s13198-022-01757-4 doi (DE-627)SPR048811629 (SPR)s13198-022-01757-4-e DE-627 ger DE-627 rakwb eng Gundewar, Swapnil K. verfasserin (orcid)0000-0002-9510-8625 aut Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. Artificial intelligence (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Bearing fault diagnosis (dpeaa)DE-He213 Discriminant classifier (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Kane, Prasad V. (orcid)0000-0002-1250-9846 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 13(2022), 6 vom: 24. Aug., Seite 2876-2894 (DE-627)SPR031222420 nnns volume:13 year:2022 number:6 day:24 month:08 pages:2876-2894 https://dx.doi.org/10.1007/s13198-022-01757-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2022 6 24 08 2876-2894 |
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10.1007/s13198-022-01757-4 doi (DE-627)SPR048811629 (SPR)s13198-022-01757-4-e DE-627 ger DE-627 rakwb eng Gundewar, Swapnil K. verfasserin (orcid)0000-0002-9510-8625 aut Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. Artificial intelligence (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Bearing fault diagnosis (dpeaa)DE-He213 Discriminant classifier (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Kane, Prasad V. (orcid)0000-0002-1250-9846 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 13(2022), 6 vom: 24. Aug., Seite 2876-2894 (DE-627)SPR031222420 nnns volume:13 year:2022 number:6 day:24 month:08 pages:2876-2894 https://dx.doi.org/10.1007/s13198-022-01757-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2022 6 24 08 2876-2894 |
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10.1007/s13198-022-01757-4 doi (DE-627)SPR048811629 (SPR)s13198-022-01757-4-e DE-627 ger DE-627 rakwb eng Gundewar, Swapnil K. verfasserin (orcid)0000-0002-9510-8625 aut Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. Artificial intelligence (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Bearing fault diagnosis (dpeaa)DE-He213 Discriminant classifier (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Kane, Prasad V. (orcid)0000-0002-1250-9846 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 13(2022), 6 vom: 24. Aug., Seite 2876-2894 (DE-627)SPR031222420 nnns volume:13 year:2022 number:6 day:24 month:08 pages:2876-2894 https://dx.doi.org/10.1007/s13198-022-01757-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2022 6 24 08 2876-2894 |
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10.1007/s13198-022-01757-4 doi (DE-627)SPR048811629 (SPR)s13198-022-01757-4-e DE-627 ger DE-627 rakwb eng Gundewar, Swapnil K. verfasserin (orcid)0000-0002-9510-8625 aut Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. Artificial intelligence (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Bearing fault diagnosis (dpeaa)DE-He213 Discriminant classifier (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Kane, Prasad V. (orcid)0000-0002-1250-9846 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 13(2022), 6 vom: 24. Aug., Seite 2876-2894 (DE-627)SPR031222420 nnns volume:13 year:2022 number:6 day:24 month:08 pages:2876-2894 https://dx.doi.org/10.1007/s13198-022-01757-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2022 6 24 08 2876-2894 |
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10.1007/s13198-022-01757-4 doi (DE-627)SPR048811629 (SPR)s13198-022-01757-4-e DE-627 ger DE-627 rakwb eng Gundewar, Swapnil K. verfasserin (orcid)0000-0002-9510-8625 aut Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. Artificial intelligence (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Bearing fault diagnosis (dpeaa)DE-He213 Discriminant classifier (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Kane, Prasad V. (orcid)0000-0002-1250-9846 aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 13(2022), 6 vom: 24. Aug., Seite 2876-2894 (DE-627)SPR031222420 nnns volume:13 year:2022 number:6 day:24 month:08 pages:2876-2894 https://dx.doi.org/10.1007/s13198-022-01757-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 2022 6 24 08 2876-2894 |
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Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier |
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Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier |
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Gundewar, Swapnil K. |
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International Journal of Systems Assurance Engineering and Management |
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Gundewar, Swapnil K. Kane, Prasad V. |
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Elektronische Aufsätze |
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Gundewar, Swapnil K. |
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10.1007/s13198-022-01757-4 |
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rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier |
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Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier |
| abstract |
Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Bearings are the principal component in the induction motor responsible for 50–60% of faults in an induction motor. Hence, detecting and diagnosing bearing faults in an induction motor is essential for reliable operation. Some soft computing techniques like artificial intelligence-based classifiers are always useful in fault diagnosis. This research diagnoses the bearing fault under three vibration signal conditions: raw vibration signal, filtered vibration signal, and wavelet-based denoised vibration signal. The statistical features such as RMS, kurtosis, standard deviation, variance, etc., are extracted from each condition. The db2 wavelet is selected based on the minimum Shannon entropy criteria for the wavelet denoising. Vibration signal data is collected from the experimental setup for four bearing conditions: healthy, outer race defect, ball defect, and cage defect. Total 1600 samples are collected from 2,000,000 data points for each condition. An artificial neural network and discriminant classifier are trained and tested for fault identification. Two other classifiers from each pedigree, i.e., support vector machine and radial basis function neural network, are also analyzed to compare the classification performance. It is observed that the ANN classifier stands the best among all, with a classification accuracy of 99.58% and a minimum computational time of 1.62 s. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Rolling element bearing fault diagnosis using supervised learning methods- artificial neural network and discriminant classifier |
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https://dx.doi.org/10.1007/s13198-022-01757-4 |
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Kane, Prasad V. |
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