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Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers

T. Jayasree1,*, R. Prem Ananth2

1 Department of Electronics & Communication Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India
2 Department of Electronics & Communication Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India

* Corresponding Author: T. Jayasree. Email: email

Sound & Vibration 2020, 54(1), 57-74. https://doi.org/10.32604/sv.2020.08573

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.

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APA Style
Jayasree, T., Ananth, R.P. (2020). Sound signal based fault classification system in motorcycles using hybrid feature sets and extreme learning machine classifiers. Sound & Vibration, 54(1), 57-74. https://doi.org/10.32604/sv.2020.08573
Vancouver Style
Jayasree T, Ananth RP. Sound signal based fault classification system in motorcycles using hybrid feature sets and extreme learning machine classifiers. Sound Vib . 2020;54(1):57-74 https://doi.org/10.32604/sv.2020.08573
IEEE Style
T. Jayasree and R.P. Ananth, "Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers," Sound Vib. , vol. 54, no. 1, pp. 57-74. 2020. https://doi.org/10.32604/sv.2020.08573



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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