Special Issue "Machine Learning in Industry 4.0: From Predictive Maintenance to Design Support Systems"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Machine Learning".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Dr. Luca Romeo
E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: machine learning; artificial intelligence; affective computing; motion analysis
Special Issues and Collections in MDPI journals
Dr. Marina Paolanti
E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: deep learning; machine learning; computer vision
Prof. Dr. Emanuele Frontoni
E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Interests: computer vision; robotics, machine learning; deep learning
Special Issues and Collections in MDPI journals
Dr. Tapio A. Heikkilä
E-Mail Website
Guest Editor
VTT Technical Research Centre of Finland, Espoo, Finland
Interests: robotics; control systems; robot control

Special Issue Information

Dear Colleagues,

Concepts such as the “Internet of Things”, “Machine Learning”, and “Artificial Intelligence” are becoming familiar to users of all ages and are helping to simplify numerous day-to-day activities. This is especially true in the case of the Industry 4.0 paradigm, where the continuous increase of available data opens the realm of possibilities to machine learning approaches. It is therefore not surprising that predictive maintenance based on machine learning approaches has quickly established itself as an industrial 4.0 use case. Implementing industrial IoT to monitor the health of industrial processes, optimize maintenance schedules, and get real-time warnings about operational risks enables manufacturers to reduce service costs, maximize uptime, and improve productivity.

This Special Issue aims to cover all aspects related to machine learning and deep learning applications in Industry 4.0, including predictive maintenance and decision support systems. All contributions related to the application and the design of data-driven methodologies related to the identification, analysis, modeling, prediction, optimization, and diagnosis of the industrial processes are particularly welcome. Our main goal is to promote the synergy between industry and academia by encouraging contributions related to a real industrial case studies.

We welcome submissions from all topics of machine learning and deep learning to any Industrial scenarios, including but not limited to the following keywords.

Dr. Luca Romeo
Dr. Marina Paolanti
Prof. Dr. Emanuele Frontoni
Dr. Tapio A. Heikkilä
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 papers will be 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. Informatics is an international peer-reviewed open access quarterly 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 1400 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

  • Industry 4.0
  • predictive maintenance
  • decision support system
  • design support system
  • machine learning
  • deep learning

Published Papers (1 paper)

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Research

Open AccessArticle
Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal
Informatics 2020, 7(4), 49; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040049 - 01 Nov 2020
Cited by 1 | Viewed by 856
Abstract
Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than [...] Read more.
Intelligent fault diagnosis methods have replaced time consuming and unreliable human analysis, increasing anomaly detection efficiency. Deep learning models are clear cut techniques for this purpose. This paper’s fundamental purpose is to automatically detect leakage in tanks during production with more reliability than a manual inspection, a common practice in industries. This research proposes an inspection system to predict tank leakage using hydrophone sensor data and deep learning algorithms after production. In this paper, leak detection was investigated using an experimental setup consisting of a plastic tank immersed underwater. Three different techniques for this purpose were implemented and compared with each other, including fast Fourier transform (FFT), wavelet transforms, and time-domain features, all of which are followed with 1D convolution neural network (1D-CNN). Applying FFT and converting the signal to a 1D image followed by 1D-CNN showed better results than other methods. Experimental results demonstrate the effectiveness and the superiority of the proposed methodology for detecting real-time leakage inaccuracy. Full article
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