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Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques

Ravikumar KN (Department of Mechanical Engineering, National Institute of Technology Karnataka, Mangalore, India)
Hemantha Kumar (Department of Mechanical Engineering, National Institute of Technology Karnataka, Mangalore, India)
Kumar GN (Department of Mechanical Engineering, National Institute of Technology Karnataka, Mangalore, India)
Gangadharan KV (Department of Mechanical Engineering, National Institute of Technology Karnataka, Mangalore, India)

Journal of Quality in Maintenance Engineering

ISSN: 1355-2511

Article publication date: 23 June 2020

Issue publication date: 27 April 2021

162

Abstract

Purpose

The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Design/methodology/approach

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

Findings

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

Practical implications

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

Originality/value

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

Keywords

Acknowledgements

The authors acknowledge the support from SOLVE: The Virtual Lab @ NITK and experimental facility provided by Centre for System Design (CSD): A Centre of excellence (http://csd.nitk.ac.in/) at National Institute of Technology Karnataka, India

Citation

KN, R., Kumar, H., GN, K. and KV, G. (2021), "Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques", Journal of Quality in Maintenance Engineering, Vol. 27 No. 2, pp. 385-412. https://doi.org/10.1108/JQME-11-2019-0109

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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