https://ojs.piscomed.com/index.php/FME <table><tbody><tr style="vertical-align: top;"><td style="text-align: justify;"><span><em>Frontiers of Mechatronical Engineering (FME)</em> is an Open Access Journal, provides a unique international and interdisciplinary forum for new research on the performance of related to all areas of mechatronical engineering. Among the leaders in its field, the journal is dedicated to the publication of high-quality papers on the fundamental properties of engineering. Always at the leading edge of research, Materials and Structures also publishes comprehensive reports prepared by the technical committees of FME.</span></td><td><div id="cover_section"><a style="font-size: 10px;" href="/index.php/fme" target="_self"><span style="color: #000000;"> <img id="cover-img" src="/public/journals/42/journalThumbnail_en_US.jpg" alt="" width="200px" align="right" /> </span> </a></div><div id="issn_section"><br /><span class="issn_num"><span class="issn_num">ISSN:2661-4073(O</span></span><span class="issn_num">)</span><br /><br /> <img src="/public/site/Open_Access.png" alt="" height="20px" /></div></td></tr></tbody></table> PiscoMed Publishing Pte Ltd en-US Frontiers of Mechatronical Engineering 2661-4073 <p>Authors contributing to this journal agree to publish their articles under the <a href="http://creativecommons.org/licenses/by-nc/4.0" target="_blank">Creative Commons Attribution-Noncommercial 4.0 International License</a>, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit, that the work is not used for commercial purposes, and that in the event of reuse or distribution, the terms of this license are made clear. With this license, the authors hold the copyright without restrictions and are allowed to retain publishing rights without restrictions as long as this journal is the original publisher of the articles.</p><p><img src="/public/site/by-nc.png" alt="" height="30px" /></p> https://ojs.piscomed.com/index.php/FME/article/view/1547 <div class="Section0"><div>Because rolling bearings have been working in an environment with complex and variable working conditions and large noise interference for a long time, the bearing fault diagnosis method has a poor diagnostic effect under variable working conditions. To solve this problem, we propose a residual neural network based on the diagnosis method of rolling bearing fault. The proposed method takes rolling bearing time-domain signal data as input. Because bearing signals have strong time-varying properties, we construct a multi-scale residual block that can not only learn features at different levels, but also expand the width and depth of the residual neural network. We use the advantages of the dilated convolution to expand the receptive field, replace part of the ordinary convolution in the multi-scale residual block with the dilated convolution, and design a multi-scale hollow residual block. The advantage is that the method is made by expanding the receptive field. It has a strong feature learning ability and can learn better features under limited data. Finally, we add a Dropout layer to discard a certain proportion of neurons after the fully connected layer, which can effectively avoid the negative impact of overfitting, and use Case Western Reserve University bearing dataset, the simulation experiment, and the SVM + EMD + Hilbert envelope spectrum, BPNN + EMD + Hilbert envelope spectrum and Resnet three ways of comparative analysis, the results show that the method under the variable condition of the fault diagnosis of rolling bearing has higher diagnosis accuracy, stronger noise resistance, and generalization ability.</div><p> </p></div><p> </p> Haopeng Liang Copyright (c) 2021 Frontiers of Mechatronical Engineering https://creativecommons.org/licenses/by-nc/4.0 2020-12-25 2020-12-25 89 96 10.18282/fme.v2i4.1547