AustralieFrV1, PascalFrancis, Corpus, bibRecord, 005A26

The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals

Identifieur interne : 005A26 ( PascalFrancis/Corpus ); précédent : 005A25; suivant : 005A27

The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals

Auteurs : R. B. Randall ; J. Antoni ; S. Chobsaard

Source :

Mechanical systems and signal processing [ 0888-3270 ] ; 2001.

RBID : Pascal:02-0056686

Descripteurs français

Pascal (Inist)
- Méthode mesure, Diagnostic panne, Détection défaut, Monitorage, Vibration, Arbre transmission, Engrenage, Palier roulement, Traitement signal, Processus non stationnaire, Analyse spectrale, Analyse statistique, Enveloppe signal, Transformation Fourier, 4680, Cyclostationnarité.

English descriptors

KwdEn :
- Cyclostationarity, Defect detection, Fault diagnostic, Fourier transformation, Gear, Measurement method, Monitoring, Non stationary process, Rolling bearing, Shaft, Signal envelope, Signal processing, Spectral analysis, Statistical analysis, Vibration.

Abstract

In recent years there has been an increasing interest in applying cyclostationary analysis to the diagnostics of machine vibration signals. This is because some machine signals, while being almost periodic, are not exactly phase-locked to shaft speeds, and thus even after compensation for speed fluctuation cannot be extracted by synchronous averaging. Typical examples are the combustion events in IC engines, which vary from cycle to cycle, and impulsive signals from faults in rolling element bearings, which are affected by minor but randomly varying slip. Two main tools for the analysis of cyclostationary signals are the two-dimensional autocorrelation function vs central time on the one axis and time displacement around the central time on the other, and its two-dimensional Fourier transform known as the spectral correlation. The latter can be quite complex to interpret, so some authors have suggested integrating it over all frequencies to obtain a Fourier series spectrum vs cyclic frequency. In this paper, it is shown that this gives the same result as a Fourier transform of the average squared envelope of the signal, which is much easier to obtain directly. Not only that, envelope analysis has long been used in the diagnostics of rolling element bearing signals, and some of the experience gained can be carried over to spectral correlation analysis. There is a possibility that the full spectral correlation may still give some advantage in distinguishing between modulation effects due to gear rotations and bearing inner race rotations (even at the same speed) by virtue of the different amounts of randomness associated with each.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A09	`01`	`1`	`ENG`	`@1 Gears and Bearings Diagnostics`
A11	`01`	`1`		`@1 RANDALL (R. B.)`
A11	`02`	`1`		`@1 ANTONI (J.)`
A11	`03`	`1`		`@1 CHOBSAARD (S.)`
A12	`01`	`1`		`@1 RANDALL (R. B.) @9 ed.`
A14	`01`			`@1 School of Mechanical and Manufacturing Engineering, The University of New South Wales @2 Sydney 2052 @3 AUS @Z 1 aut.`
A14	`02`			`@1 Laboratoire d'Analyse des Signaux et des Processus Industriels (LASPI), IUT de Roanne @2 42334 Roanne @3 FRA @Z 2 aut.`
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C01	`01`		`ENG`	@0 In recent years there has been an increasing interest in applying cyclostationary analysis to the diagnostics of machine vibration signals. This is because some machine signals, while being almost periodic, are not exactly phase-locked to shaft speeds, and thus even after compensation for speed fluctuation cannot be extracted by synchronous averaging. Typical examples are the combustion events in IC engines, which vary from cycle to cycle, and impulsive signals from faults in rolling element bearings, which are affected by minor but randomly varying slip. Two main tools for the analysis of cyclostationary signals are the two-dimensional autocorrelation function vs central time on the one axis and time displacement around the central time on the other, and its two-dimensional Fourier transform known as the spectral correlation. The latter can be quite complex to interpret, so some authors have suggested integrating it over all frequencies to obtain a Fourier series spectrum vs cyclic frequency. In this paper, it is shown that this gives the same result as a Fourier transform of the average squared envelope of the signal, which is much easier to obtain directly. Not only that, envelope analysis has long been used in the diagnostics of rolling element bearing signals, and some of the experience gained can be carried over to spectral correlation analysis. There is a possibility that the full spectral correlation may still give some advantage in distinguishing between modulation effects due to gear rotations and bearing inner race rotations (even at the same speed) by virtue of the different amounts of randomness associated with each.
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C03	`01`	`X`	`ENG`	`@0 Measurement method @5 01`
C03	`01`	`X`	`SPA`	`@0 Método medida @5 01`
C03	`02`	`X`	`FRE`	`@0 Diagnostic panne @5 02`
C03	`02`	`X`	`ENG`	`@0 Fault diagnostic @5 02`
C03	`02`	`X`	`SPA`	`@0 Diagnóstico pana @5 02`
C03	`03`	`X`	`FRE`	`@0 Détection défaut @5 03`
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C03	`03`	`X`	`SPA`	`@0 Detección imperfección @5 03`
C03	`04`	`X`	`FRE`	`@0 Monitorage @5 04`
C03	`04`	`X`	`ENG`	`@0 Monitoring @5 04`
C03	`04`	`X`	`SPA`	`@0 Monitoreo @5 04`
C03	`05`	`X`	`FRE`	`@0 Vibration @5 05`
C03	`05`	`X`	`ENG`	`@0 Vibration @5 05`
C03	`05`	`X`	`SPA`	`@0 Vibración @5 05`
C03	`06`	`X`	`FRE`	`@0 Arbre transmission @5 06`
C03	`06`	`X`	`ENG`	`@0 Shaft @5 06`
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C03	`07`	`X`	`FRE`	`@0 Engrenage @5 09`
C03	`07`	`X`	`ENG`	`@0 Gear @5 09`
C03	`07`	`X`	`SPA`	`@0 Engranaje @5 09`
C03	`08`	`X`	`FRE`	`@0 Palier roulement @5 10`
C03	`08`	`X`	`ENG`	`@0 Rolling bearing @5 10`
C03	`08`	`X`	`SPA`	`@0 Cojinete rodillos @5 10`
C03	`09`	`X`	`FRE`	`@0 Traitement signal @5 15`
C03	`09`	`X`	`ENG`	`@0 Signal processing @5 15`
C03	`09`	`X`	`SPA`	`@0 Procesamiento señal @5 15`
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C03	`10`	`X`	`ENG`	`@0 Non stationary process @5 16`
C03	`10`	`X`	`SPA`	`@0 Proceso no estacionario @5 16`
C03	`11`	`X`	`FRE`	`@0 Analyse spectrale @5 17`
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C03	`15`	`X`	`FRE`	`@0 4680 @2 PAC @4 INC @5 57`
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C03	`16`	`X`	`ENG`	`@0 Cyclostationarity @4 CD @5 96`
N21				`@1 028`

Format Inist (serveur)

NO :	PASCAL 02-0056686 INIST
ET :	The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals
AU :	RANDALL (R. B.); ANTONI (J.); CHOBSAARD (S.); RANDALL (R. B.)
AF :	School of Mechanical and Manufacturing Engineering, The University of New South Wales/Sydney 2052/Australie (1 aut.); Laboratoire d'Analyse des Signaux et des Processus Industriels (LASPI), IUT de Roanne/42334 Roanne/France (2 aut.); Department of Engineering Development, Royal Thai Naval Dockyard/Thaïlande (3 aut.); The University of New South Wales/Sydney, NSW 2052/Australie (1 aut.)
DT :	Publication en série; Niveau analytique
SO :	Mechanical systems and signal processing; ISSN 0888-3270; Royaume-Uni; Da. 2001; Vol. 15; No. 5; Pp. 945-962; Bibl. 8 ref.
LA :	Anglais
EA :	In recent years there has been an increasing interest in applying cyclostationary analysis to the diagnostics of machine vibration signals. This is because some machine signals, while being almost periodic, are not exactly phase-locked to shaft speeds, and thus even after compensation for speed fluctuation cannot be extracted by synchronous averaging. Typical examples are the combustion events in IC engines, which vary from cycle to cycle, and impulsive signals from faults in rolling element bearings, which are affected by minor but randomly varying slip. Two main tools for the analysis of cyclostationary signals are the two-dimensional autocorrelation function vs central time on the one axis and time displacement around the central time on the other, and its two-dimensional Fourier transform known as the spectral correlation. The latter can be quite complex to interpret, so some authors have suggested integrating it over all frequencies to obtain a Fourier series spectrum vs cyclic frequency. In this paper, it is shown that this gives the same result as a Fourier transform of the average squared envelope of the signal, which is much easier to obtain directly. Not only that, envelope analysis has long been used in the diagnostics of rolling element bearing signals, and some of the experience gained can be carried over to spectral correlation analysis. There is a possibility that the full spectral correlation may still give some advantage in distinguishing between modulation effects due to gear rotations and bearing inner race rotations (even at the same speed) by virtue of the different amounts of randomness associated with each.
CC :	001D12C01; 001B40F30R
FD :	Méthode mesure; Diagnostic panne; Détection défaut; Monitorage; Vibration; Arbre transmission; Engrenage; Palier roulement; Traitement signal; Processus non stationnaire; Analyse spectrale; Analyse statistique; Enveloppe signal; Transformation Fourier; 4680; Cyclostationnarité
ED :	Measurement method; Fault diagnostic; Defect detection; Monitoring; Vibration; Shaft; Gear; Rolling bearing; Signal processing; Non stationary process; Spectral analysis; Statistical analysis; Signal envelope; Fourier transformation; Cyclostationarity
SD :	Método medida; Diagnóstico pana; Detección imperfección; Monitoreo; Vibración; Arbol transmisión; Engranaje; Cojinete rodillos; Procesamiento señal; Proceso no estacionario; Análisis espectral; Análisis estadístico; Envoltura señal; Transformación Fourier
LO :	INIST-21404.354000103085500070
ID :	02-0056686

Links to Exploration step

Pascal:02-0056686

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<front><div type="abstract" xml:lang="en">In recent years there has been an increasing interest in applying cyclostationary analysis to the diagnostics of machine vibration signals. This is because some machine signals, while being almost periodic, are not exactly phase-locked to shaft speeds, and thus even after compensation for speed fluctuation cannot be extracted by synchronous averaging. Typical examples are the combustion events in IC engines, which vary from cycle to cycle, and impulsive signals from faults in rolling element bearings, which are affected by minor but randomly varying slip. Two main tools for the analysis of cyclostationary signals are the two-dimensional autocorrelation function vs central time on the one axis and time displacement around the central time on the other, and its two-dimensional Fourier transform known as the spectral correlation. The latter can be quite complex to interpret, so some authors have suggested integrating it over all frequencies to obtain a Fourier series spectrum vs cyclic frequency. In this paper, it is shown that this gives the same result as a Fourier transform of the average squared envelope of the signal, which is much easier to obtain directly. Not only that, envelope analysis has long been used in the diagnostics of rolling element bearing signals, and some of the experience gained can be carried over to spectral correlation analysis. There is a possibility that the full spectral correlation may still give some advantage in distinguishing between modulation effects due to gear rotations and bearing inner race rotations (even at the same speed) by virtue of the different amounts of randomness associated with each.</div>
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<s5>09</s5>
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<fC03 i1="07" i2="X" l="ENG"><s0>Gear</s0>
<s5>09</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Engranaje</s0>
<s5>09</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Palier roulement</s0>
<s5>10</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Rolling bearing</s0>
<s5>10</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Cojinete rodillos</s0>
<s5>10</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Traitement signal</s0>
<s5>15</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Signal processing</s0>
<s5>15</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Procesamiento señal</s0>
<s5>15</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Processus non stationnaire</s0>
<s5>16</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Non stationary process</s0>
<s5>16</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Proceso no estacionario</s0>
<s5>16</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Analyse spectrale</s0>
<s5>17</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Spectral analysis</s0>
<s5>17</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Análisis espectral</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Analyse statistique</s0>
<s5>18</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Statistical analysis</s0>
<s5>18</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Análisis estadístico</s0>
<s5>18</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Enveloppe signal</s0>
<s5>19</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Signal envelope</s0>
<s5>19</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Envoltura señal</s0>
<s5>19</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Transformation Fourier</s0>
<s5>20</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Fourier transformation</s0>
<s5>20</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Transformación Fourier</s0>
<s5>20</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>4680</s0>
<s2>PAC</s2>
<s4>INC</s4>
<s5>57</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Cyclostationnarité</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Cyclostationarity</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fN21><s1>028</s1>
</fN21>
</pA>
</standard>
<server><NO>PASCAL 02-0056686 INIST</NO>
<ET>The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals</ET>
<AU>RANDALL (R. B.); ANTONI (J.); CHOBSAARD (S.); RANDALL (R. B.)</AU>
<AF>School of Mechanical and Manufacturing Engineering, The University of New South Wales/Sydney 2052/Australie (1 aut.); Laboratoire d'Analyse des Signaux et des Processus Industriels (LASPI), IUT de Roanne/42334 Roanne/France (2 aut.); Department of Engineering Development, Royal Thai Naval Dockyard/Thaïlande (3 aut.); The University of New South Wales/Sydney, NSW 2052/Australie (1 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Mechanical systems and signal processing; ISSN 0888-3270; Royaume-Uni; Da. 2001; Vol. 15; No. 5; Pp. 945-962; Bibl. 8 ref.</SO>
<LA>Anglais</LA>
<EA>In recent years there has been an increasing interest in applying cyclostationary analysis to the diagnostics of machine vibration signals. This is because some machine signals, while being almost periodic, are not exactly phase-locked to shaft speeds, and thus even after compensation for speed fluctuation cannot be extracted by synchronous averaging. Typical examples are the combustion events in IC engines, which vary from cycle to cycle, and impulsive signals from faults in rolling element bearings, which are affected by minor but randomly varying slip. Two main tools for the analysis of cyclostationary signals are the two-dimensional autocorrelation function vs central time on the one axis and time displacement around the central time on the other, and its two-dimensional Fourier transform known as the spectral correlation. The latter can be quite complex to interpret, so some authors have suggested integrating it over all frequencies to obtain a Fourier series spectrum vs cyclic frequency. In this paper, it is shown that this gives the same result as a Fourier transform of the average squared envelope of the signal, which is much easier to obtain directly. Not only that, envelope analysis has long been used in the diagnostics of rolling element bearing signals, and some of the experience gained can be carried over to spectral correlation analysis. There is a possibility that the full spectral correlation may still give some advantage in distinguishing between modulation effects due to gear rotations and bearing inner race rotations (even at the same speed) by virtue of the different amounts of randomness associated with each.</EA>
<CC>001D12C01; 001B40F30R</CC>
<FD>Méthode mesure; Diagnostic panne; Détection défaut; Monitorage; Vibration; Arbre transmission; Engrenage; Palier roulement; Traitement signal; Processus non stationnaire; Analyse spectrale; Analyse statistique; Enveloppe signal; Transformation Fourier; 4680; Cyclostationnarité</FD>
<ED>Measurement method; Fault diagnostic; Defect detection; Monitoring; Vibration; Shaft; Gear; Rolling bearing; Signal processing; Non stationary process; Spectral analysis; Statistical analysis; Signal envelope; Fourier transformation; Cyclostationarity</ED>
<SD>Método medida; Diagnóstico pana; Detección imperfección; Monitoreo; Vibración; Arbol transmisión; Engranaje; Cojinete rodillos; Procesamiento señal; Proceso no estacionario; Análisis espectral; Análisis estadístico; Envoltura señal; Transformación Fourier</SD>
<LO>INIST-21404.354000103085500070</LO>
<ID>02-0056686</ID>
</server>
</inist>
</record>

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The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals

The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals

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