Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection
Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wyłomańska, Agnieszka [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Formation of stacking fault tetrahedron in single-crystal Cu during nanoindentation investigated by molecular dynamics - Liu, Qitao ELSEVIER, 2017, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:128 ; year:2017 ; day:15 ; month:12 ; pages:14-22 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.apacoust.2016.11.008 |
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ELV030755670 |
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| 520 | |a Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. | ||
| 520 | |a Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. | ||
| 650 | 7 | |a α -stable distribution |2 Elsevier | |
| 650 | 7 | |a Tempered stable distribution |2 Elsevier | |
| 650 | 7 | |a Local damage detection |2 Elsevier | |
| 650 | 7 | |a Informative frequency band selection |2 Elsevier | |
| 650 | 7 | |a Early stage |2 Elsevier | |
| 700 | 1 | |a Żak, Grzegorz |4 oth | |
| 700 | 1 | |a Kruczek, Piotr |4 oth | |
| 700 | 1 | |a Zimroz, Radosław |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Qitao ELSEVIER |t Formation of stacking fault tetrahedron in single-crystal Cu during nanoindentation investigated by molecular dynamics |d 2017 |g Amsterdam [u.a.] |w (DE-627)ELV020429711 |
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10.1016/j.apacoust.2016.11.008 doi GBVA2017019000025.pica (DE-627)ELV030755670 (ELSEVIER)S0003-682X(16)30441-8 DE-627 ger DE-627 rakwb eng 530 530 DE-600 530 VZ 600 670 530 VZ 51.00 bkl Wyłomańska, Agnieszka verfasserin aut Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. α -stable distribution Elsevier Tempered stable distribution Elsevier Local damage detection Elsevier Informative frequency band selection Elsevier Early stage Elsevier Żak, Grzegorz oth Kruczek, Piotr oth Zimroz, Radosław oth Enthalten in Elsevier Liu, Qitao ELSEVIER Formation of stacking fault tetrahedron in single-crystal Cu during nanoindentation investigated by molecular dynamics 2017 Amsterdam [u.a.] (DE-627)ELV020429711 volume:128 year:2017 day:15 month:12 pages:14-22 extent:9 https://doi.org/10.1016/j.apacoust.2016.11.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 51.00 Werkstoffkunde: Allgemeines VZ AR 128 2017 15 1215 14-22 9 045F 530 |
| spelling |
10.1016/j.apacoust.2016.11.008 doi GBVA2017019000025.pica (DE-627)ELV030755670 (ELSEVIER)S0003-682X(16)30441-8 DE-627 ger DE-627 rakwb eng 530 530 DE-600 530 VZ 600 670 530 VZ 51.00 bkl Wyłomańska, Agnieszka verfasserin aut Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. α -stable distribution Elsevier Tempered stable distribution Elsevier Local damage detection Elsevier Informative frequency band selection Elsevier Early stage Elsevier Żak, Grzegorz oth Kruczek, Piotr oth Zimroz, Radosław oth Enthalten in Elsevier Liu, Qitao ELSEVIER Formation of stacking fault tetrahedron in single-crystal Cu during nanoindentation investigated by molecular dynamics 2017 Amsterdam [u.a.] (DE-627)ELV020429711 volume:128 year:2017 day:15 month:12 pages:14-22 extent:9 https://doi.org/10.1016/j.apacoust.2016.11.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 51.00 Werkstoffkunde: Allgemeines VZ AR 128 2017 15 1215 14-22 9 045F 530 |
| allfields_unstemmed |
10.1016/j.apacoust.2016.11.008 doi GBVA2017019000025.pica (DE-627)ELV030755670 (ELSEVIER)S0003-682X(16)30441-8 DE-627 ger DE-627 rakwb eng 530 530 DE-600 530 VZ 600 670 530 VZ 51.00 bkl Wyłomańska, Agnieszka verfasserin aut Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. α -stable distribution Elsevier Tempered stable distribution Elsevier Local damage detection Elsevier Informative frequency band selection Elsevier Early stage Elsevier Żak, Grzegorz oth Kruczek, Piotr oth Zimroz, Radosław oth Enthalten in Elsevier Liu, Qitao ELSEVIER Formation of stacking fault tetrahedron in single-crystal Cu during nanoindentation investigated by molecular dynamics 2017 Amsterdam [u.a.] (DE-627)ELV020429711 volume:128 year:2017 day:15 month:12 pages:14-22 extent:9 https://doi.org/10.1016/j.apacoust.2016.11.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 51.00 Werkstoffkunde: Allgemeines VZ AR 128 2017 15 1215 14-22 9 045F 530 |
| allfieldsGer |
10.1016/j.apacoust.2016.11.008 doi GBVA2017019000025.pica (DE-627)ELV030755670 (ELSEVIER)S0003-682X(16)30441-8 DE-627 ger DE-627 rakwb eng 530 530 DE-600 530 VZ 600 670 530 VZ 51.00 bkl Wyłomańska, Agnieszka verfasserin aut Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. α -stable distribution Elsevier Tempered stable distribution Elsevier Local damage detection Elsevier Informative frequency band selection Elsevier Early stage Elsevier Żak, Grzegorz oth Kruczek, Piotr oth Zimroz, Radosław oth Enthalten in Elsevier Liu, Qitao ELSEVIER Formation of stacking fault tetrahedron in single-crystal Cu during nanoindentation investigated by molecular dynamics 2017 Amsterdam [u.a.] (DE-627)ELV020429711 volume:128 year:2017 day:15 month:12 pages:14-22 extent:9 https://doi.org/10.1016/j.apacoust.2016.11.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 51.00 Werkstoffkunde: Allgemeines VZ AR 128 2017 15 1215 14-22 9 045F 530 |
| allfieldsSound |
10.1016/j.apacoust.2016.11.008 doi GBVA2017019000025.pica (DE-627)ELV030755670 (ELSEVIER)S0003-682X(16)30441-8 DE-627 ger DE-627 rakwb eng 530 530 DE-600 530 VZ 600 670 530 VZ 51.00 bkl Wyłomańska, Agnieszka verfasserin aut Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. α -stable distribution Elsevier Tempered stable distribution Elsevier Local damage detection Elsevier Informative frequency band selection Elsevier Early stage Elsevier Żak, Grzegorz oth Kruczek, Piotr oth Zimroz, Radosław oth Enthalten in Elsevier Liu, Qitao ELSEVIER Formation of stacking fault tetrahedron in single-crystal Cu during nanoindentation investigated by molecular dynamics 2017 Amsterdam [u.a.] (DE-627)ELV020429711 volume:128 year:2017 day:15 month:12 pages:14-22 extent:9 https://doi.org/10.1016/j.apacoust.2016.11.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 51.00 Werkstoffkunde: Allgemeines VZ AR 128 2017 15 1215 14-22 9 045F 530 |
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Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection |
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Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. |
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Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. |
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Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV030755670</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625182618.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.apacoust.2016.11.008</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2017019000025.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV030755670</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0003-682X(16)30441-8</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="082" ind1="0" ind2=" "><subfield code="a">530</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="a">670</subfield><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">51.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wyłomańska, Agnieszka</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Application of tempered stable distribution for selection of optimal frequency band in gearbox local damage detection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">9</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Local damage detection methods based on the vibration signal analysis are widely discussed in the literature. One of the most popular approach is signal enhancement by filtering focused on extraction of informative content at so called informative frequency band (IFB). In such approach the vibration signal first is decomposed into time-frequency representation and then the measures of impulsivity are applied to the appropriate sub-signals. Till now, kurtosis was preferred as criterion for IFB search. However, for some cases more robust criteria should be used. Jablonski proposed to calculate kurtosis from envelope spectrum, Urbanek suggested MID (Modulation Intensity Distribution), set of alternative selectors as extension of spectral kurtosis (SK) have been defined by Obuchowski. Further extension was recently proposed by Zak, namely statistics related to α -stable distribution were used as IFB indicators. This distribution is especially important in modeling data with outliers so it was reasonable to apply this approach in the considered problem. As it was shown, the α -stable based methodology for some vibration signals more clearly indicates the IFB in contrast to the classical ones. However, in case of early stage of fault development or developed fault with high level of noise the mentioned criteria (including stability index as a measure of impulsivity) may be insufficient. It is related to the fact that cyclic impulses related to damage are often hidden in the noise (even after time-frequency decomposition). In this paper we propose to apply (instead of kurtosis or stability index) the parameters of tempered stable distribution. This distribution is an extension of the α -stable one, however, it possesses many properties of Gaussian systems. It is associated with two parameters which may indicate properly the IFB in case of poor signal-to-noise ratio. In this paper we remind the approach of IFB selection based on the kurtosis and α -stable distribution and describe how to localize the information of the fault by using tempered stable approach. The real vibration signal from gearbox is analyzed in the context of presented methodology. Finally, we examine two examples of vibration signals for which the proposed approach is superior with respect to the classical methodology.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">α -stable distribution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Tempered stable distribution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Local damage detection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Informative frequency band selection</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Early stage</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Żak, Grzegorz</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kruczek, Piotr</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zimroz, Radosław</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Qitao ELSEVIER</subfield><subfield code="t">Formation of stacking fault tetrahedron in single-crystal Cu during nanoindentation investigated by molecular dynamics</subfield><subfield code="d">2017</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV020429711</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:128</subfield><subfield code="g">year:2017</subfield><subfield code="g">day:15</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:14-22</subfield><subfield code="g">extent:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.apacoust.2016.11.008</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">51.00</subfield><subfield code="j">Werkstoffkunde: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">128</subfield><subfield code="j">2017</subfield><subfield code="b">15</subfield><subfield code="c">1215</subfield><subfield code="h">14-22</subfield><subfield code="g">9</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">530</subfield></datafield></record></collection>
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