Description

Frontiers of Mechatronical Engineering (FME) is an Open Access Journal, providing a unique international and interdisciplinary forum for new research on the performance of all relevant 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. 

The scope of FME includes but is not limited to:

  • mechanical engineering
  • automatics
  • electromechanical system
  • nuclear engineering
  • industrial engineering
  • nanotechnology
  • production engineering
  • computer intelligence
  • robotics
  • aerospace engineering
  • system design & modeling
  • building material

Latest Articles

  • Open Access

    Article

    Article ID: 1547

    Rolling Bearing Fault Diagnosis based on Residual Neural Network

    by Haopeng Liang

    Frontiers of Mechatronical Engineering, Vol.2, No.4, 2020; 314 Views, 49 PDF Downloads

    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.    

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Call for Papers for the Special Issue: Frontiers of Mechatronical Engineering

2019-05-09

This special issue seeks manuscripts that presents and addresses the emerging novel discoveries,important insights the computational intelligence models,such as deep and knowledge learning,reasoning,and decision making.It also includes in-depth studies of innovation artificial intelligence approaches that are being used in the real domain.It welcomes interdisciplinary approaches including but not limited to artificial intelligence and machine learning,cognitive psychology,posing interesting and timely research challenges in computational intelligence.

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Sallam Osman Fageeri Khairy

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