This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig. Various traditional machine learning and deep learning methods have been proposed for multivariate time-series classification, including distance-based, functional data-oriented, feature-driven, and convolution kernel-based methods. Recent studies have shown using convolution kernel-based methods like ROCKET, and 1D convolutional neural networks with ResNet and FCN, have robust performance for multivariate time-series data classification. We propose an ensemble of three convolution kernel-based methods and show its efficacy on this fault detection problem by outperforming other approaches and achieving an accuracy of more than 98.8\%.
@article{arxiv.2305.05532,
title = {An ensemble of convolution-based methods for fault detection using vibration signals},
author = {Xian Yeow Lee and Aman Kumar and Lasitha Vidyaratne and Aniruddha Rajendra Rao and Ahmed Farahat and Chetan Gupta},
journal= {arXiv preprint arXiv:2305.05532},
year = {2023}
}
Comments
12 Pages, 9 Figures, 2 Tables. Accepted at ICPHM 2023