Special Issue "Disruptive Trends in Automation Technology"

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

Deadline for manuscript submissions: 30 April 2023 | Viewed by 1648

Special Issue Editors

Dr. Seppo Sierla
E-Mail Website
Guest Editor
Department of Electrical Engineering and Automation, Aalto University, P.O. Box 15500, FI-00076 Aalto, Finland
Interests: simulation; digital twin; virtual power plant; demand response; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. David Hästbacka
E-Mail Website
Guest Editor
Dept. of Automation Science and Engineering, Tampere University of Technology, Tampere, Finland
Interests: control systems; industrial automation; automation; system modeling; systems engineering; system integration; service oriented architecture; Internet of Things; software architecture; semantic web; technologies
Dr. Kai Zenger
E-Mail Website
Guest Editor
Department of Electrical Engineering and Automation, Aalto University, Aalto, Finland
Interests: control systems engineering

Special Issue Information

Dear Colleagues,

The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain. For example, reinforcement learning is emerging as an alternative technology for industrial process control and optimization, and machine learning is heavily applied to fault diagnostic and predictive maintenance. Real-time connectivity, cloudification, big data and artificial intelligence are all driving the transformation of conventional simulators to digital twins.

In this Special Issue, we welcome contributions on advances in automation technology, especially but not limited to the abovementioned disruptive developments. Survey papers and reviews are also welcomed.

Dr. Seppo Sierla
Prof. Dr. David Hästbacka
Dr. Kai Zenger
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. 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 2300 CHF (Swiss Francs). For papers submitted to this special issue through the collaboration with the Automation Days 2023 conference (https://www.automaatioseura.fi/automationdays2023/), the APC is 1955 CHF. 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

  • automation
  • control technology
  • simulation
  • digital twin
  • cloud
  • edge
  • big data
  • artificial intelligence
  • machine learning
  • reinforcement learning

Published Papers (2 papers)

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Research

Article
From DevOps to MLOps: Overview and Application to Electricity Market Forecasting
Appl. Sci. 2022, 12(19), 9851; https://doi.org/10.3390/app12199851 - 30 Sep 2022
Viewed by 552
Abstract
In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has been proven to deliver reliable, scalable software within a shorter time. Due to the explosion of Machine Learning (ML) applications, the term Machine Learning Operations (MLOps) has gained significant interest among [...] Read more.
In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has been proven to deliver reliable, scalable software within a shorter time. Due to the explosion of Machine Learning (ML) applications, the term Machine Learning Operations (MLOps) has gained significant interest among ML practitioners. This paper explains the DevOps and MLOps processes relevant to the implementation of MLOps. The contribution of this paper towards the MLOps framework is threefold: First, we review the state of the art in MLOps by analyzing the related work in MLOps. Second, we present an overview of the leading DevOps principles relevant to MLOps. Third, we derive an MLOps framework from the MLOps theory and apply it to a time-series forecasting application in the hourly day-ahead electricity market. The paper concludes with how MLOps could be generalized and applied to two more use cases with minor changes. Full article
(This article belongs to the Special Issue Disruptive Trends in Automation Technology)
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Article
Whitening CNN-Based Rotor System Fault Diagnosis Model Features
Appl. Sci. 2022, 12(9), 4411; https://doi.org/10.3390/app12094411 - 27 Apr 2022
Cited by 1 | Viewed by 489
Abstract
Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these [...] Read more.
Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models. Full article
(This article belongs to the Special Issue Disruptive Trends in Automation Technology)
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