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
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Artificial Intelligence Computer Science - Machine Learning Statistics - Applications Statistics - Machine Learning
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
Externí odkaz:	http://arxiv.org/abs/2305.05532 Zobrazit plný text záznamu View this record from Arxiv