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Machines
Special Issues
Application of Deep Learning in Intelligent Machines
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Application of Deep Learning in Intelligent Machines

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1824

Special Issue Editors

Dr. Lie Yang

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: intelligent vehicles; computer vision; deep learning; brain–computer interface; pattern recognition
Dr. Chen Lyu

E-Mail Website
Guest Editor
Associate Professor, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: automated driving; human–machine systems; intelligent electric vehicles; human–robot collaboration; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Qingchao Liu

E-Mail Website
Guest Editor
Associate Professor, Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China
Interests: driving behavior analysis; path planning; autonomous vehicle; cooperative adaptive cruise control; traffic flow theory; intelligent transportation systems
Dr. Zilong Zhang

E-Mail Website
Guest Editor
Associate Professor, School of Computer Science and Technology, Hainan University, Haikou, China
Interests: artificial intelligence; human–computer interaction; bioinformatics

Special Issue Information

Dear Colleagues,

In recent years, the application of deep learning algorithms in the development of intelligent machines has achieved remarkable advancements. Deep learning, which is a subset of artificial intelligence (AI), has demonstrated its potential to revolutionize various domains. For example, some computer vision-based deep learning algorithms can automatically extract hierarchical features from images, enabling machines to recognize objects, detect anomalies, and classify complex scenes. The integration of deep learning algorithms into robotics and autonomous systems has led to a new era of intelligent machines. Autonomous vehicles, for instance, employ deep neural networks to process sensor data, recognize road signs, and make real-time decisions, thus enhancing road safety and efficiency. In manufacturing, robots equipped with deep learning capabilities can perform complex tasks with precision, adapt to changing environments, and learn by observing human demonstrations.

This Special Issue is focused on the study and discovery of the applications of deep learning algorithms in intelligent machines, which mainly include intelligent robotics, autonomous driving, intelligent fault diagnosis, intelligent assembly, intelligent manufacturing, process optimization, supply chain management, and safety. Deep learning algorithms have become the key components of intelligent machines. Their ability to learn from data, adapt to different tasks, and provide superior performance has enabled for the development of intelligent machines. As technology continues to advance, the integration of deep learning into intelligent machines promises to reshape industries and improve quality of life for individuals worldwide.

Dr. Lie Yang
Dr. Chen Lyu
Dr. Qingchao Liu
Dr. Zilong Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent machines
  • deep learning
  • intelligent robotics
  • autonomous driving
  • intelligent fault diagnosis
  • intelligent assembly
  • intelligent manufacturing
  • process optimization
  • intelligent management of supply chain
  • safety monitoring and forecasting

Published Papers (1 paper)

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Research

17 pages, 4492 KiB  
Article
Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks
by Xiaopeng Liu, Jianfeng Hong, Kang Zhao, Bingxiang Sun, Weige Zhang and Jiuchun Jiang
Machines 2023, 11(12), 1061; https://doi.org/10.3390/machines11121061 - 29 Nov 2023
Viewed by 1352
Abstract
Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract [...] Read more.
Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Machines)
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