An ensemble of convolution-based methods for fault detection using vibration signals
Autor: | Lee, Xian Yeow, Kumar, Aman, Vidyaratne, Lasitha, Rao, Aniruddha Rajendra, Farahat, Ahmed, Gupta, Chetan |
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Rok vydání: | 2023 |
Předmět: | |
Zdroj: | 2023 IEEE International Conference on Prognostics and Health Management (ICPHM) |
Druh dokumentu: | Working Paper |
DOI: | 10.1109/ICPHM57936.2023.10194112 |
Popis: | 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\%. Comment: 12 Pages, 9 Figures, 2 Tables. Accepted at ICPHM 2023 |
Databáze: | arXiv |
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