@Article{sv.2020.08573,
AUTHOR = {T. Jayasree, R. Prem Ananth},
TITLE = {Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers},
JOURNAL = {Sound \& Vibration},
VOLUME = {54},
YEAR = {2020},
NUMBER = {1},
PAGES = {57--74},
URL = {http://www.techscience.com/sv/v54n1/38410},
ISSN = {2693-1443},
ABSTRACT = {Vehicles generate dissimilar sound patterns under different working
environments. These generated sound patterns signify the condition of the
engines, which in turn is used for diagnosing various faults. In this paper, the
sound signals produced by motorcycles are analyzed to locate various faults.
The important attributes are extracted from the generated sound signals based
on time, frequency and wavelet domains which clearly describe the statistical
behavior of the signals. Further, various types of faults are classified using the
Extreme Learning Machine (ELM) classifier from the extracted features. Moreover,
the improved classification performance is obtained by the combination of
feature sets in different domains. The simulation results clearly demonstrate that
the proposed hybrid feature set together with the ELM classifier gives more promising
results with higher classification accuracy when compared with the other
conventional methods.},
DOI = {10.32604/sv.2020.08573}
}