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Informatics
Special Issues
Machine Learning in Industry 4.0: From Predictive Maintenance to Design...
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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: closed (30 November 2021) | Viewed by 16558

Special Issue Editors

Dr. Luca Romeo

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Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: machine learning; artificial intelligence; affective computing; motion analysis
Special Issues, Collections and Topics 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, 60121 Ancona, Italy
Interests: computer vision; robotics, machine learning; deep learning
Special Issues, Collections and Topics 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 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. 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 1800 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 (4 papers)

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Research

18 pages, 860 KiB  
Article
Optimization of Food Industry Production Using the Monte Carlo Simulation Method: A Case Study of a Meat Processing Plant
by Mikhail Koroteev, Ekaterina Romanova, Dmitriy Korovin, Vasiliy Shevtsov, Vadim Feklin, Petr Nikitin, Sergey Makrushin and Konstantin V. Bublikov
Informatics 2022, 9(1), 5; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics9010005 - 18 Jan 2022
Cited by 6 | Viewed by 4200
Abstract
The problem evaluated in this study is related to the optimization of a budget of an industrial enterprise using simulation methods of the production process. Our goal is to offer a universal and straightforward methodology for simulating a production budget at any level [...] Read more.
The problem evaluated in this study is related to the optimization of a budget of an industrial enterprise using simulation methods of the production process. Our goal is to offer a universal and straightforward methodology for simulating a production budget at any level of complexity by presenting it in a specific form. The calculation of such production schemes, in most enterprises, is currently done manually, which significantly limits the possibilities for optimization. This article proposes a model based on the Monte Carlo method to automate the budgeting process. The application of this model is described using an example of a typical meat processing enterprise. Approbation of the model showed its high applicability and the ability to transform the process of making management decisions and the potential to increase the profits of the enterprise, which is unattainable using other methods. As a result of the study, we present a methodology for modeling industrial production that can significantly speed up the formation and optimization of an enterprise’s budget. In our demonstration case, the profit increased by over 30 percentage points. Full article
(This article belongs to the Special Issue Machine Learning in Industry 4.0: From Predictive Maintenance to Design Support Systems)
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13 pages, 1030 KiB  
Article
Identifying Benchmarks for Failure Prediction in Industry 4.0
by Mouhamadou Saliou Diallo, Sid Ahmed Mokeddem, Agnès Braud, Gabriel Frey and Nicolas Lachiche
Informatics 2021, 8(4), 68; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040068 - 30 Sep 2021
Cited by 8 | Viewed by 3564
Abstract
Industry 4.0 is characterized by the availability of sensors to operate the so-called intelligent factory. Predictive maintenance, in particular, failure prediction, is an important issue to cut the costs associated with production breaks. We studied more than 40 publications on predictive maintenance. We [...] Read more.
Industry 4.0 is characterized by the availability of sensors to operate the so-called intelligent factory. Predictive maintenance, in particular, failure prediction, is an important issue to cut the costs associated with production breaks. We studied more than 40 publications on predictive maintenance. We point out that they focus on various machine learning algorithms rather than on the selection of suitable datasets. In fact, most publications consider a single, usually non-public, benchmark. More benchmarks are needed to design and test the generality of the proposed approaches. This paper is the first to define the requirements on these benchmarks. It highlights that there are only two benchmarks that can be used for supervised learning among the six publicly available ones we found in the literature. We also illustrate how such a benchmark can be used with deep learning to successfully train and evaluate a failure prediction model. We raise several perspectives for research. Full article
(This article belongs to the Special Issue Machine Learning in Industry 4.0: From Predictive Maintenance to Design Support Systems)
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14 pages, 261 KiB  
Article
Embracing Industry 4.0: Empirical Insights from Malaysia
by Mansoor Ahmed Soomro, Mohd Hizam-Hanafiah, Nor Liza Abdullah, Mohd Helmi Ali and Muhammad Shahar Jusoh
Informatics 2021, 8(2), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8020030 - 22 Apr 2021
Cited by 10 | Viewed by 3501
Abstract
Industry 4.0 revolution, with its cutting-edge technologies, is an enabler for businesses, particularly in reducing the cost and improving the productivity. However, a large number of organizations are still too in their infancy to leverage the true potential of Industry 4.0 and its [...] Read more.
Industry 4.0 revolution, with its cutting-edge technologies, is an enabler for businesses, particularly in reducing the cost and improving the productivity. However, a large number of organizations are still too in their infancy to leverage the true potential of Industry 4.0 and its technologies. This paper takes a quantitative approach to reveal key insights from the companies that have implemented Industry 4.0 technologies. For this purpose, 238 technology companies in Malaysia were studied through a survey questionnaire. As technology companies are usually the first in line to adopt new technologies, they can be studied better as leaders in adopting the latest technologies. The findings of this descriptive study surfaced an array of insights in terms of Industry 4.0 readiness, Industry 4.0 technologies, leadership, strategy, and innovation. This research paper contributes by providing 10 key empirical insights on Industry 4.0 that can be utilized by managers to pace up their efforts towards digital transformation, and can help the policymakers in drafting the right policy to drive the digital revolution. Full article
(This article belongs to the Special Issue Machine Learning in Industry 4.0: From Predictive Maintenance to Design Support Systems)
18 pages, 5401 KiB  
Article
Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal
by Masoumeh Rahimi, Alireza Alghassi, Mominul Ahsan and Julfikar Haider
Informatics 2020, 7(4), 49; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040049 - 01 Nov 2020
Cited by 13 | Viewed by 4046
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
(This article belongs to the Special Issue Machine Learning in Industry 4.0: From Predictive Maintenance to Design Support Systems)
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