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A transient enhancement method for two-stage helical gearbox fault diagnosis based on ALE

Tian, Xiange, Wang, Tie, Chen, Zhi, Gu, Fengshou and Ball, Andrew (2013) A transient enhancement method for two-stage helical gearbox fault diagnosis based on ALE. Journal of Signal and Information Processing, 4 (3B). pp. 132-137. ISSN 2159-4481

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Abstract

Periodical impulse component is one of typical fault characteristics in vibration signals from rotating machinery. However,
this component is very small in the early stage of the fault and masked by various noises such as gear meshing
components modulated by shaft frequency, which make it difficult to extract accurately for fault detection. The adaptive
line enhancer (ALE) is an effective technique for separating sinusoidals from broad-band components of an input signal
for detecting the presence of sinusoids in white noise. In this paper, ALE is explored to suppress the periodical gear
meshing frequencies and enhance the fault feature impulses for more accurate fault diagnosis. The results obtained from
simulated and experimental vibration signals of a two stage helical gearbox prove that the ALE method is very effective
in reducing the periodical gear meshing noise and making the impulses in vibration very clear in the time-frequency
analysis. The results show a clear difference between the baseline and 30% tooth damage of a helical gear which has not
been detected successfully in author’s previous studies.

Item Type: Article
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
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Depositing User: Cherry Edmunds
Date Deposited: 06 Aug 2013 16:00
Last Modified: 05 Nov 2015 11:12
URI: http://eprints.hud.ac.uk/id/eprint/18088

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