Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning
Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the anal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gan, Meng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
Quality factor based signal decomposition |
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| Anmerkung: |
© Springer Science+Business Media New York 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent manufacturing - Springer US, 1990, 29(2015), 2 vom: 10. Juli, Seite 463-480 |
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| Übergeordnetes Werk: |
volume:29 ; year:2015 ; number:2 ; day:10 ; month:07 ; pages:463-480 |
| Links: |
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| DOI / URN: |
10.1007/s10845-015-1125-6 |
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OLC2066776610 |
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| 520 | |a Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. | ||
| 650 | 4 | |a Oscillatory waveform | |
| 650 | 4 | |a Quality factor based signal decomposition | |
| 650 | 4 | |a Manifold learning | |
| 650 | 4 | |a Impulse-enhanced signature (IES) | |
| 650 | 4 | |a Machinery fault diagnosis | |
| 700 | 1 | |a Wang, Cong |4 aut | |
| 700 | 1 | |a Zhu, Chang’an |4 aut | |
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10.1007/s10845-015-1125-6 doi (DE-627)OLC2066776610 (DE-He213)s10845-015-1125-6-p DE-627 ger DE-627 rakwb eng 620 004 VZ Gan, Meng verfasserin aut Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. Oscillatory waveform Quality factor based signal decomposition Manifold learning Impulse-enhanced signature (IES) Machinery fault diagnosis Wang, Cong aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 29(2015), 2 vom: 10. Juli, Seite 463-480 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:29 year:2015 number:2 day:10 month:07 pages:463-480 https://doi.org/10.1007/s10845-015-1125-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 29 2015 2 10 07 463-480 |
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10.1007/s10845-015-1125-6 doi (DE-627)OLC2066776610 (DE-He213)s10845-015-1125-6-p DE-627 ger DE-627 rakwb eng 620 004 VZ Gan, Meng verfasserin aut Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. Oscillatory waveform Quality factor based signal decomposition Manifold learning Impulse-enhanced signature (IES) Machinery fault diagnosis Wang, Cong aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 29(2015), 2 vom: 10. Juli, Seite 463-480 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:29 year:2015 number:2 day:10 month:07 pages:463-480 https://doi.org/10.1007/s10845-015-1125-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 29 2015 2 10 07 463-480 |
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10.1007/s10845-015-1125-6 doi (DE-627)OLC2066776610 (DE-He213)s10845-015-1125-6-p DE-627 ger DE-627 rakwb eng 620 004 VZ Gan, Meng verfasserin aut Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. Oscillatory waveform Quality factor based signal decomposition Manifold learning Impulse-enhanced signature (IES) Machinery fault diagnosis Wang, Cong aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 29(2015), 2 vom: 10. Juli, Seite 463-480 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:29 year:2015 number:2 day:10 month:07 pages:463-480 https://doi.org/10.1007/s10845-015-1125-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 29 2015 2 10 07 463-480 |
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10.1007/s10845-015-1125-6 doi (DE-627)OLC2066776610 (DE-He213)s10845-015-1125-6-p DE-627 ger DE-627 rakwb eng 620 004 VZ Gan, Meng verfasserin aut Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. Oscillatory waveform Quality factor based signal decomposition Manifold learning Impulse-enhanced signature (IES) Machinery fault diagnosis Wang, Cong aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 29(2015), 2 vom: 10. Juli, Seite 463-480 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:29 year:2015 number:2 day:10 month:07 pages:463-480 https://doi.org/10.1007/s10845-015-1125-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 29 2015 2 10 07 463-480 |
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10.1007/s10845-015-1125-6 doi (DE-627)OLC2066776610 (DE-He213)s10845-015-1125-6-p DE-627 ger DE-627 rakwb eng 620 004 VZ Gan, Meng verfasserin aut Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. Oscillatory waveform Quality factor based signal decomposition Manifold learning Impulse-enhanced signature (IES) Machinery fault diagnosis Wang, Cong aut Zhu, Chang’an aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 29(2015), 2 vom: 10. Juli, Seite 463-480 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:29 year:2015 number:2 day:10 month:07 pages:463-480 https://doi.org/10.1007/s10845-015-1125-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 29 2015 2 10 07 463-480 |
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Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning |
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Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. © Springer Science+Business Media New York 2015 |
| abstractGer |
Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. © Springer Science+Business Media New York 2015 |
| abstract_unstemmed |
Abstract This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults. © Springer Science+Business Media New York 2015 |
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Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning |
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Wang, Cong Zhu, Chang’an |
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