Abstract
Gearbox defects have been considered as one of the major causes of failure in rotating machinery. It is important to identify and diagnose the actual reasons behind the failure of gearbox for a reliable operation of equipment that relies such a system. In this paper, a fault diagnosis method based on entropy-based feature and support vector machine (SVM) has been proposed for detecting the faults in bearings and gear set in the gearbox. Initially, different features in terms of time domain as well as time-frequency domain have been extracted and classified via SVM. The proposed method has been validated by the publicly available online dataset which is consists of nine classes (eight types of faults and healthy) with load and unloaded conditions. The optimum validation accuracy (98.84% for 20-0 load condition and 99.87% for 30-2 load conditions) has been obtained by the entropy-based feature extracted from transformed continuous wavelet transform (CWT) signal. The outcome of this study is very encouraging since it emphasizes to avoid the computational complexity in feature extraction as well as classification.
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Hasan, M.J. et al. (2021). Gearbox Fault Diagnostics: An Examination on the Efficacy of Different Feature Extraction Techniques. In: Mat Jizat, J.A., et al. Advances in Robotics, Automation and Data Analytics. iCITES 2020. Advances in Intelligent Systems and Computing, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-70917-4_39
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DOI: https://doi.org/10.1007/978-3-030-70917-4_39
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