@inproceedings{d98d1b64e6d34e189a261ca2de987d98,
title = "Bearing Fault Detection with a Deep Light Weight CNN",
abstract = "Bearings are vital part of rotary machines. A failure of bearing has a negative impact on schedules, production operation and even human casualties. Therefore, in prior achieving fault detection and diagnosis (FDD) of bearing is ensuring the safety and reliable operation of rotating machinery systems. However, there are some challenges of the industrial FDD problems. Since according to a literature review, more than half of the broken machines are caused by bearing fault. Therefore, one of the important thing is time delay should be reduced for FDD. However, due to many learnable parameters in model and data of long sequence, both lead to time delay for FDD. Therefore, this paper proposes a deep Light Convolutional Neural Network (LCNN) using one dimensional convolution neural network for FDD.",
keywords = "Bearing, CNN, Data augmentation, Fault diagnosis, Light",
author = "Oh, \{Jin Woo\} and Jongpil Jeong",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 20th International Conference on Computational Science and Its Applications, ICCSA 2020 ; Conference date: 01-07-2020 Through 04-07-2020",
year = "2020",
doi = "10.1007/978-3-030-58802-1\_43",
language = "English",
isbn = "9783030588014",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "604--612",
editor = "Osvaldo Gervasi and Beniamino Murgante and Sanjay Misra and Chiara Garau and Ivan Blecic and David Taniar and Apduhan, \{Bernady O.\} and Rocha, \{Ana Maria A.C.\} and Eufemia Tarantino and Torre, \{Carmelo Maria\} and Yeliz Karaca",
booktitle = "Computational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings",
}