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Fault diagnosis of helical gearbox using acoustic signal and wavelets
Pranesh S.K, , , Amarnath M.
Published in IOP Publishing
2017
Volume: 197
   
Issue: 1
Abstract
The efficient transmission of power in machines is needed and gears are an appropriate choice. Faults in gears result in loss of energy and money. The monitoring and fault diagnosis are done by analysis of the acoustic and vibrational signals which are generally considered to be unwanted by products. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Artificial faults were created and subsequently signals were captured by a microphone. An extensive study using different wavelet transformations for feature extraction from the acoustic signals was done, followed by waveletselection and feature selection using J48 decision tree and feature classification was performed using K star algorithm. Classification accuracy of 100% was obtained in the study. © Published under licence by IOP Publishing Ltd.
About the journal
JournalData powered by TypesetIOP Conference Series: Materials Science and Engineering
PublisherData powered by TypesetIOP Publishing
ISSN1757-8981
Open AccessNo