Abstract
More often than not, gearbox defects have been reported in the literature to be one of the primary causes of rotating machinery failure. In this paper, we explore different types of time-domain as well as frequency domain features towards the classification of gearbox fault diagnostics via Support Vector Machine (SVM). The proposed architecture was evaluated on an online repository dataset which comprises nine classes in which eight are faulty under both loaded and unloaded environments. It was shown from the study that the fast standard deviation-based feature extracted from the Fast-Fourier based transformed signals could yield a classification accuracy of 99.4% and 98.69% for both training and testing dataset, respectively on the 20 Hz-0V loading condition. The preliminary results presented here are non-trivial towards achieving low computational expense-based gearbox fault diagnostics.
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Hasan, M.J. et al. (2021). Gearbox Fault Diagnostics: An Evaluation of Fast-Fourier Transform-Based Extracted Features with Support Vector Machine Classifier. In: Chew, E., et al. RiTA 2020. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-4803-8_40
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DOI: https://doi.org/10.1007/978-981-16-4803-8_40
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