Engine gearbox fault diagnosis using machine learning approach
Journal of Quality in Maintenance Engineering
ISSN: 1355-2511
Article publication date: 13 August 2018
Abstract
Purpose
Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues.
Design/methodology/approach
This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm.
Findings
The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis.
Originality/value
This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.
Keywords
Acknowledgements
The authors acknowledge the funding support from SOLVE: The Virtual Lab @ NITK (www.solve.nitk.ac.in) and experimental facility provided by Centre for System Design (CSD): A Centre of excellence at NITK-Surathkal. The authors also acknowledge the help rendered by Dr V. Sugumaran who is Associate Professor at VIT University, Chennai.
Citation
Vernekar, K., Kumar, H. and K.V., G. (2018), "Engine gearbox fault diagnosis using machine learning approach", Journal of Quality in Maintenance Engineering, Vol. 24 No. 3, pp. 345-357. https://doi.org/10.1108/JQME-11-2015-0058
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited