Open Access

ARTICLE

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

T. Jayasree^1,*, 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:

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

Received 09 September 2019; Accepted 31 December 2019; Issue published 01 March 2020

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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Keywords

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