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arXiv · 2305.05532

An ensemble of convolution-based methods for fault detection using vibration signals

Xian Yeow Lee·Aman Kumar·Lasitha Vidyaratne·Aniruddha Rajendra Rao·Ahmed Farahat·Chetan Gupta
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Abstract

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\%.

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Xian Yeow Lee, Aman Kumar, Lasitha Vidyaratne, Aniruddha Rajendra Rao, Ahmed Farahat, Chetan Gupta. 2023-05-05. An ensemble of convolution-based methods for fault detection using vibration signals. https://doi.org/10.1109/icphm57936.2023.10194112

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