Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning

Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based...
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Gespeichert in:

Autor*in:	Iqbal, Mohmad [verfasserIn] Madan, A. K.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	CNC machine tools Bearing fault Gentle AdaBoost Machine learning

Anmerkung:	© Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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.

Übergeordnetes Werk:	Enthalten in: Journal of vibration engineering & technologies - Singapore : Springer Singapore, 2018, 12(2023), 2 vom: 07. März, Seite 1621-1634
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:2 ; day:07 ; month:03 ; pages:1621-1634

Links:	Volltext

DOI / URN:	10.1007/s42417-023-00930-8

Katalog-ID:	SPR055082297

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520			\|a Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals.
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allfields	10.1007/s42417-023-00930-8 doi (DE-627)SPR055082297 (SPR)s42417-023-00930-8-e DE-627 ger DE-627 rakwb eng Iqbal, Mohmad verfasserin (orcid)0000-0003-0606-9650 aut Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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. Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. CNC machine tools (dpeaa)DE-He213 Bearing fault (dpeaa)DE-He213 Gentle AdaBoost (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Madan, A. K. aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 2 vom: 07. März, Seite 1621-1634 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:2 day:07 month:03 pages:1621-1634 https://dx.doi.org/10.1007/s42417-023-00930-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 2 07 03 1621-1634
spelling	10.1007/s42417-023-00930-8 doi (DE-627)SPR055082297 (SPR)s42417-023-00930-8-e DE-627 ger DE-627 rakwb eng Iqbal, Mohmad verfasserin (orcid)0000-0003-0606-9650 aut Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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. Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. CNC machine tools (dpeaa)DE-He213 Bearing fault (dpeaa)DE-He213 Gentle AdaBoost (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Madan, A. K. aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 2 vom: 07. März, Seite 1621-1634 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:2 day:07 month:03 pages:1621-1634 https://dx.doi.org/10.1007/s42417-023-00930-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 2 07 03 1621-1634
allfields_unstemmed	10.1007/s42417-023-00930-8 doi (DE-627)SPR055082297 (SPR)s42417-023-00930-8-e DE-627 ger DE-627 rakwb eng Iqbal, Mohmad verfasserin (orcid)0000-0003-0606-9650 aut Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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. Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. CNC machine tools (dpeaa)DE-He213 Bearing fault (dpeaa)DE-He213 Gentle AdaBoost (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Madan, A. K. aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 2 vom: 07. März, Seite 1621-1634 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:2 day:07 month:03 pages:1621-1634 https://dx.doi.org/10.1007/s42417-023-00930-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 2 07 03 1621-1634
allfieldsGer	10.1007/s42417-023-00930-8 doi (DE-627)SPR055082297 (SPR)s42417-023-00930-8-e DE-627 ger DE-627 rakwb eng Iqbal, Mohmad verfasserin (orcid)0000-0003-0606-9650 aut Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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. Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. CNC machine tools (dpeaa)DE-He213 Bearing fault (dpeaa)DE-He213 Gentle AdaBoost (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Madan, A. K. aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 2 vom: 07. März, Seite 1621-1634 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:2 day:07 month:03 pages:1621-1634 https://dx.doi.org/10.1007/s42417-023-00930-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 2 07 03 1621-1634
allfieldsSound	10.1007/s42417-023-00930-8 doi (DE-627)SPR055082297 (SPR)s42417-023-00930-8-e DE-627 ger DE-627 rakwb eng Iqbal, Mohmad verfasserin (orcid)0000-0003-0606-9650 aut Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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. Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. CNC machine tools (dpeaa)DE-He213 Bearing fault (dpeaa)DE-He213 Gentle AdaBoost (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Madan, A. K. aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 2 vom: 07. März, Seite 1621-1634 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:2 day:07 month:03 pages:1621-1634 https://dx.doi.org/10.1007/s42417-023-00930-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 2 07 03 1621-1634
language	English
source	Enthalten in Journal of vibration engineering & technologies 12(2023), 2 vom: 07. März, Seite 1621-1634 volume:12 year:2023 number:2 day:07 month:03 pages:1621-1634
sourceStr	Enthalten in Journal of vibration engineering & technologies 12(2023), 2 vom: 07. März, Seite 1621-1634 volume:12 year:2023 number:2 day:07 month:03 pages:1621-1634
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	CNC machine tools Bearing fault Gentle AdaBoost Machine learning
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container_title	Journal of vibration engineering & technologies
authorswithroles_txt_mv	Iqbal, Mohmad @@aut@@ Madan, A. K. @@aut@@
publishDateDaySort_date	2023-03-07T00:00:00Z
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Springer Nature or its licensor (e.g. a society or other partner) 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. 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author	Iqbal, Mohmad
spellingShingle	Iqbal, Mohmad misc CNC machine tools misc Bearing fault misc Gentle AdaBoost misc Machine learning Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning
authorStr	Iqbal, Mohmad
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topic_title	Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning CNC machine tools (dpeaa)DE-He213 Bearing fault (dpeaa)DE-He213 Gentle AdaBoost (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213
topic	misc CNC machine tools misc Bearing fault misc Gentle AdaBoost misc Machine learning
topic_unstemmed	misc CNC machine tools misc Bearing fault misc Gentle AdaBoost misc Machine learning
topic_browse	misc CNC machine tools misc Bearing fault misc Gentle AdaBoost misc Machine learning
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title	Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning
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title_full	Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning
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journal	Journal of vibration engineering & technologies
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author_browse	Iqbal, Mohmad Madan, A. K.
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author-letter	Iqbal, Mohmad
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title_sort	bearing fault diagnosis in cnc machine using hybrid signal decomposition and gentle adaboost learning
title_auth	Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning
abstract	Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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	Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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	Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals. © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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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title_short	Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning
url	https://dx.doi.org/10.1007/s42417-023-00930-8
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author2	Madan, A. K.
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up_date	2024-07-04T04:06:01.002Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR055082297</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240309064744.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240309s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42417-023-00930-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR055082297</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42417-023-00930-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Iqbal, Mohmad</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0606-9650</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine. Methods Fault diagnosis was made using Hybrid Signal Decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools. Results The proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine. Conclusion The success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CNC machine tools</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bearing fault</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gentle AdaBoost</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Madan, A. K.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of vibration engineering & technologies</subfield><subfield code="d">Singapore : Springer Singapore, 2018</subfield><subfield code="g">12(2023), 2 vom: 07. März, Seite 1621-1634</subfield><subfield code="w">(DE-627)1030123837</subfield><subfield code="w">(DE-600)2941414-3</subfield><subfield code="x">2523-3939</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:12</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:2</subfield><subfield code="g">day:07</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:1621-1634</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s42417-023-00930-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield 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Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning

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