Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal
Abstract Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Saufi, Syahril Ramadhan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 14(2022), 4 vom: 05. Okt., Seite 3827-3838 |
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| Übergeordnetes Werk: |
volume:14 ; year:2022 ; number:4 ; day:05 ; month:10 ; pages:3827-3838 |
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| DOI / URN: |
10.1007/s12652-022-04436-1 |
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| Katalog-ID: |
SPR049872966 |
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| 520 | |a Abstract Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. | ||
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| 700 | 1 | |a Ahmad, Zair Asrar |4 aut | |
| 700 | 1 | |a Hasan, M. Danial Abu |4 aut | |
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10.1007/s12652-022-04436-1 doi (DE-627)SPR049872966 (SPR)s12652-022-04436-1-e DE-627 ger DE-627 rakwb eng Saufi, Syahril Ramadhan verfasserin (orcid)0000-0003-1317-7860 aut Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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 Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. Fault diagnosis (dpeaa)DE-He213 Grey wolf optimiser (dpeaa)DE-He213 Hybrid activation function (dpeaa)DE-He213 Deep sparse autoencoder (dpeaa)DE-He213 Isham, Muhammad Firdaus aut Ahmad, Zair Asrar aut Hasan, M. Danial Abu aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 4 vom: 05. Okt., Seite 3827-3838 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:4 day:05 month:10 pages:3827-3838 https://dx.doi.org/10.1007/s12652-022-04436-1 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2119 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_4277 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 14 2022 4 05 10 3827-3838 |
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10.1007/s12652-022-04436-1 doi (DE-627)SPR049872966 (SPR)s12652-022-04436-1-e DE-627 ger DE-627 rakwb eng Saufi, Syahril Ramadhan verfasserin (orcid)0000-0003-1317-7860 aut Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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 Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. Fault diagnosis (dpeaa)DE-He213 Grey wolf optimiser (dpeaa)DE-He213 Hybrid activation function (dpeaa)DE-He213 Deep sparse autoencoder (dpeaa)DE-He213 Isham, Muhammad Firdaus aut Ahmad, Zair Asrar aut Hasan, M. Danial Abu aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 4 vom: 05. Okt., Seite 3827-3838 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:4 day:05 month:10 pages:3827-3838 https://dx.doi.org/10.1007/s12652-022-04436-1 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2119 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_4277 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 14 2022 4 05 10 3827-3838 |
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10.1007/s12652-022-04436-1 doi (DE-627)SPR049872966 (SPR)s12652-022-04436-1-e DE-627 ger DE-627 rakwb eng Saufi, Syahril Ramadhan verfasserin (orcid)0000-0003-1317-7860 aut Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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 Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. Fault diagnosis (dpeaa)DE-He213 Grey wolf optimiser (dpeaa)DE-He213 Hybrid activation function (dpeaa)DE-He213 Deep sparse autoencoder (dpeaa)DE-He213 Isham, Muhammad Firdaus aut Ahmad, Zair Asrar aut Hasan, M. Danial Abu aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 4 vom: 05. Okt., Seite 3827-3838 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:4 day:05 month:10 pages:3827-3838 https://dx.doi.org/10.1007/s12652-022-04436-1 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2119 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_4277 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 14 2022 4 05 10 3827-3838 |
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10.1007/s12652-022-04436-1 doi (DE-627)SPR049872966 (SPR)s12652-022-04436-1-e DE-627 ger DE-627 rakwb eng Saufi, Syahril Ramadhan verfasserin (orcid)0000-0003-1317-7860 aut Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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 Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. Fault diagnosis (dpeaa)DE-He213 Grey wolf optimiser (dpeaa)DE-He213 Hybrid activation function (dpeaa)DE-He213 Deep sparse autoencoder (dpeaa)DE-He213 Isham, Muhammad Firdaus aut Ahmad, Zair Asrar aut Hasan, M. Danial Abu aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 4 vom: 05. Okt., Seite 3827-3838 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:4 day:05 month:10 pages:3827-3838 https://dx.doi.org/10.1007/s12652-022-04436-1 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2119 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_4277 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 14 2022 4 05 10 3827-3838 |
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10.1007/s12652-022-04436-1 doi (DE-627)SPR049872966 (SPR)s12652-022-04436-1-e DE-627 ger DE-627 rakwb eng Saufi, Syahril Ramadhan verfasserin (orcid)0000-0003-1317-7860 aut Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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 Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. Fault diagnosis (dpeaa)DE-He213 Grey wolf optimiser (dpeaa)DE-He213 Hybrid activation function (dpeaa)DE-He213 Deep sparse autoencoder (dpeaa)DE-He213 Isham, Muhammad Firdaus aut Ahmad, Zair Asrar aut Hasan, M. Danial Abu aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 14(2022), 4 vom: 05. Okt., Seite 3827-3838 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:14 year:2022 number:4 day:05 month:10 pages:3827-3838 https://dx.doi.org/10.1007/s12652-022-04436-1 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2119 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_4277 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 14 2022 4 05 10 3827-3838 |
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fault diagnosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Grey wolf optimiser</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybrid activation function</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep sparse autoencoder</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Isham, Muhammad Firdaus</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ahmad, Zair Asrar</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hasan, M. 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Saufi, Syahril Ramadhan |
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Saufi, Syahril Ramadhan misc Fault diagnosis misc Grey wolf optimiser misc Hybrid activation function misc Deep sparse autoencoder Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal |
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Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal Fault diagnosis (dpeaa)DE-He213 Grey wolf optimiser (dpeaa)DE-He213 Hybrid activation function (dpeaa)DE-He213 Deep sparse autoencoder (dpeaa)DE-He213 |
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Saufi, Syahril Ramadhan |
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machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal |
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Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal |
| abstract |
Abstract Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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 Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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 Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of mechanical components such as bearings, gears, and blades. The main challenge of IFD using traditional methods lies in finding the good features that reflect the machine conditions that need prior knowledge and essential expertise to identify the features. In order to solve this problem, this paper introduces a new IFD technique based on a deep sparse autoencoder (DSAE) using a raw vibration time-domain signal (1D time-series signal). A novel method called modified hybrid DSAE was developed to avoid the need for feature extraction and selection steps. First, a resilient backpropagation learning algorithm was employed on the three hidden layers of the DSAE network. Then, a combination of sigmoid and rectified linear unit (RELU) activation functions was proposed for the DSAE network. Finally, the hyperparameters of the DSAE network were optimized using the grey wolf optimizer technique (GWO). The proposed method was applied to analyze the 1D time-series signal of five machinery datasets, including three bearings, one gearbox, and one turbine blade. The analysis proved that the diagnosis performance achieved by the proposed model is highly reliable and applicable to fault diagnosis of machinery components. The model achieved 100 per cent diagnosis accuracy on four datasets (MFPT, CWRU, gearbox, turbine blade) and 95 per cent diagnosis accuracy on the MaFaulDa dataset. The results from the proposed model show superior diagnostic accuracy compared to other related studies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 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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| title_short |
Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal |
| url |
https://dx.doi.org/10.1007/s12652-022-04436-1 |
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| author2 |
Isham, Muhammad Firdaus Ahmad, Zair Asrar Hasan, M. Danial Abu |
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Isham, Muhammad Firdaus Ahmad, Zair Asrar Hasan, M. Danial Abu |
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| doi_str |
10.1007/s12652-022-04436-1 |
| up_date |
2024-07-04T02:37:15.526Z |
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| score |
7.401618 |