Two stage helical gearbox fault detection and diagnosis based on continuous wavelet transformation of time synchronous averaged vibration signals

F. Elbarghathi, T. Wang, D. Zhen, F. Gu, A. Ball

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.

LanguageEnglish
Article number012083
JournalJournal of Physics: Conference Series
Volume364
Issue number1
DOIs
Publication statusPublished - 2012
Event25th International Congress on Condition Monitoring and Diagnostic Engineering: Sustained Prosperity through Proactive Monitoring, Diagnosis and Management - University of Huddersfield, Huddersfield, United Kingdom
Duration: 18 Jun 201220 Jun 2012
Conference number: 25
http://compeng.hud.ac.uk/comadem2012/ (Link to Conference Website )

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transmissions (machine elements)
fault detection
vibration
coefficients

Cite this

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Two stage helical gearbox fault detection and diagnosis based on continuous wavelet transformation of time synchronous averaged vibration signals. / Elbarghathi, F.; Wang, T.; Zhen, D.; Gu, F.; Ball, A.

In: Journal of Physics: Conference Series, Vol. 364, No. 1, 012083, 2012.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Two stage helical gearbox fault detection and diagnosis based on continuous wavelet transformation of time synchronous averaged vibration signals

AU - Elbarghathi, F.

AU - Wang, T.

AU - Zhen, D.

AU - Gu, F.

AU - Ball, A.

PY - 2012

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N2 - Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.

AB - Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.

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KW - Helical gearbox

KW - Time synchronous average

KW - Wavelet Transformation

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