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Applied Sciences
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Intelligent Manufacturing and Medical-Engineering Integration
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Intelligent Manufacturing and Medical-Engineering Integration

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1985

Special Issue Editors

Prof. Dr. Haisong Huang

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Guest Editor
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
Interests: intelligent manufacturing; manufacturing big data and manufacturing information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jianan Wei

E-Mail Website
Guest Editor
Key Laboratory of Modern Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
Interests: manufacturing big data and manufacturing information systems; intelligent manufacturing; machine learning; deep transfer learning; fault diagnosis; imbalanced data processing and predictive maintenance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Long Wen

E-Mail Website
Guest Editor
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Interests: deep learning; automatic machine learning; fault diagnosis; intelligent algorithm
Special Issues, Collections and Topics in MDPI journals
Dr. Zhuyun Chen

E-Mail Website
Guest Editor
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Interests: deep transfer learning; federated learning; signal processing; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore research and applications in cutting-edge fields such as Manufacturing Big Data and Information Systems, Intelligent Manufacturing and Maintenance, and Medical-Engineering Integration Manufacturing. With the intelligent transformation of the manufacturing industry, big data technology is playing an increasingly important role in production processes. This Special Issue will focus on how to apply advanced technologies like big data analytics, machine learning, and deep learning to achieve process optimization, fault prediction and maintenance, and resource utilization maximization in manufacturing. Additionally, this Special Issue will also address the cross-disciplinary domains of intelligent manufacturing and medical-engineering integration, investigating the application of intelligent manufacturing technologies in the manufacturing and operation of medical devices to enhance the efficiency and quality of the medical industry. Scholars from academia and industry are welcome to participate and share the latest research findings and innovative ideas in the fields of intelligent manufacturing and medical-engineering integration.

Potential topics include, but are not limited to:

  • Intelligent fault prediction and prevention driven by big data: Utilizing big data analysis and machine learning to achieve early prediction and prevention of equipment failures, optimizing production processes, and reducing maintenance costs.
  • Intelligent fault diagnosis models and algorithms: Researching algorithms like imbalanced learning, few-shot learning, positive-unlabeled learning (PU learning), zero-shot learning, and modeling methods under various operating conditions to provide efficient and accurate solutions for mechanical equipment fault diagnosis.
  • Cross-disciplinary innovation in medical-engineering integration manufacturing: Applying intelligent manufacturing technology to the manufacturing and operation of medical devices to improve the efficiency and quality of the medical industry.
  • Advancements in fault diagnosis/prediction and remaining useful life estimation using deep learning algorithms: Exploring the application of deep learning in data analysis and remaining useful life prediction for achieving more accurate predictive maintenance.
  • Data-driven medical diagnosis: Utilizing deep learning and data analysis techniques for intelligent diagnosis of medical images and patient data to enhance the accuracy and efficiency of medical diagnosis.
  • Construction and optimization of manufacturing information systems: Building intelligent manufacturing information systems and incorporating technologies like imbalanced data processing, time series data analysis, and natural language processing to optimize manufacturing processes, improving efficiency and quality in manufacturing.

Prof. Dr. Haisong Huang
Dr. Jianan Wei
Prof. Dr. Long Wen
Dr. Zhuyun Chen
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 manufacturing
  • medical-engineering integration manufacturing
  • intelligent maintenance
  • manufacturing big data

Related Special Issue

Published Papers (2 papers)

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Research

14 pages, 3376 KiB  
Article
Wind Turbine Blade Cracking Detection under Imbalanced Data Using a Novel Roundtrip Auto-Encoder Approach
by Yuyan Zhang, Yafeng Zhang, Hao Li, Lingdi Yan, Xiaoyu Wen and Haoqi Wang
Appl. Sci. 2023, 13(21), 11628; https://doi.org/10.3390/app132111628 - 24 Oct 2023
Viewed by 640
Abstract
Imbalanced data cause low recognition of wind turbine blade cracking. Existing data-level augmentation methods, including sampling and generative strategies, may yield lots of high-confidence but low-value samples, which fail to improve the detection of blade cracking. Therefore, this paper designs a novel RTAE [...] Read more.
Imbalanced data cause low recognition of wind turbine blade cracking. Existing data-level augmentation methods, including sampling and generative strategies, may yield lots of high-confidence but low-value samples, which fail to improve the detection of blade cracking. Therefore, this paper designs a novel RTAE (roundtrip auto-encoder) method. Based on the idea of the roundtrip approach, we design two generator networks and two discriminator networks to ensure the cycle mapping between cracking samples and latent variables. Further, by leveraging cycle consistency loss, generated samples fit the distribution of historical cracking samples well. Thus, these generated samples effectively realize data augmentation and improve recognition of blade cracking. Additionally, we apply an auto-encoder method to reduce the dimension of historical samples and thus the complexity of the generator network and discriminator network. Through the analysis of real wind turbine blade cracking data, the recognition of cracking samples is improved by 19.8%, 23.8% and 22.7% for precision, recall and F1-score. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Medical-Engineering Integration)
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21 pages, 3633 KiB  
Article
Dynamic Anomaly Detection in Gantry Transactions Using Graph Convolutional Network-Gate Recurrent Unit with Adaptive Attention
by Fumin Zou, Yue Xing, Qiang Ren, Feng Guo, Zhaoyi Zhou and Zihan Ye
Appl. Sci. 2023, 13(19), 11068; https://doi.org/10.3390/app131911068 - 08 Oct 2023
Viewed by 796
Abstract
With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph [...] Read more.
With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph Convolutional Network-Gate Recurrent Unit (GCN-GRU) dynamic anomaly detection method for gantry transactions. In this paper, four different classes of gantry anomalies are defined and modeled, representing gantries as nodes and the connectivity between gantries as edges. First, the spatial distribution of highway ETC gantries is modeled using the GCN model to extract gantry node features. Then, the contextual attention mechanism is utilized to capture the recent patterns of the dynamic transaction graph of the gantries, and the GRU model is used to extract the time-series characteristics of the gantry nodes to dynamically update the gantry leakage. Our model is evaluated on several experimental datasets and compared with other commonly used anomaly detection methods. The experimental results show that our model outperforms other anomaly detection models in terms of accuracy, precision, and other evaluation values of 99%, proving its effectiveness and robustness. This model has a wide application potential in real gantry detection and management. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Medical-Engineering Integration)
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