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Processes
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Tools and Algorithms for Industrial System Production and Operation Management
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Tools and Algorithms for Industrial System Production and Operation Management

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 6859

Special Issue Editors

Dr. Ammar Al-Jodah

E-Mail Website
Guest Editor
School of Physics, Maths and Computing, Physics, The University of Western Australia, Perth, WA 6907, Australia
Interests: control systems; sensing; precision engineering; robotics
Prof. Dr. Amjad J. Humaidi

E-Mail Website
Guest Editor
Department of Control and Systems Engineering, University of Technology, Baghdad 10001, Iraq
Interests: control theory; nonlinear control; robotic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industry 4.0 revolution took production systems and processes to a whole new era in the digital world. The integration of modern algorithms and tools in these cyberphysical systems allowed for exponential growth in production to meet market demands. Automating tasks, optimizing processes, and using novel machine learning techniques and algorithms will introduce significant progress in Industry 4.0. The ease of acquiring, transferring, and processing data either locally or in the cloud has opened a wide range of opportunities to employ affordable yet effective digital tools in the manufacturing field. However, this brings a plethora of research questions and problems that require cutting-edge solutions in engineering, computer science, management, and many other fields.

In this Special Issue, we invite articles focused on research regarding the latest tools, algorithms, and operation management approaches that can be used to improve production systems. This Special Issue will focus on publishing original research works on the application of optimization algorithms, machine learning, and artificial intelligence toward improving industrial systems. Furthermore, it is concerned with new tools in operation management for automating/optimizing tasks, systems, and processes. The aim of this Special Issue is to report the state of the art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing in this field. It also welcomes studies that stimulate research discussion on moving toward a particular industrial sector.

Topics of interest in this Special Issue include but are not limited to:

  • Optimization algorithms for industrial system production;
  • Machine learning applications for industrial systems and devices;
  • Artificial intelligence in a production environment;
  • Automation and cyberphysical systems;
  • Novel control methods and algorithms for industrial systems and robotics;
  • Applications of advanced algorithms toward improving production processes;
  • New tools in precision industrial systems;
  • Sensing techniques, methods, and algorithms for industrial systems;
  • Operations management tools for production.

Dr. Ammar Al-Jodah
Prof. Dr. Amjad J. Humaidi
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. Processes 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

  • machine learning
  • optimization
  • automation
  • operation management

Published Papers (3 papers)

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Research

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18 pages, 1860 KiB  
Article
Improved Fault Classification for Predictive Maintenance in Industrial IoT Based on AutoML: A Case Study of Ball-Bearing Faults
by Russul H. Hadi, Haider N. Hady, Ahmed M. Hasan, Ammar Al-Jodah and Amjad J. Humaidi
Processes 2023, 11(5), 1507; https://doi.org/10.3390/pr11051507 - 15 May 2023
Cited by 22 | Viewed by 2237
Abstract
The growing complexity of data derived from Industrial Internet of Things (IIoT) systems presents substantial challenges for traditional machine-learning techniques, which struggle to effectively manage the needs of predictive maintenance applications. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning [...] Read more.
The growing complexity of data derived from Industrial Internet of Things (IIoT) systems presents substantial challenges for traditional machine-learning techniques, which struggle to effectively manage the needs of predictive maintenance applications. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process, reducing the necessity for manual hyperparameter tuning and computational resources, thereby positioning themselves as a potentially transformative innovation in the Industry 4.0 era. This research introduces two distinct models: AutoML, employing PyCaret, and Auto Deep Neural Network (AutoDNN), utilizing AutoKeras, both aimed at accurately identifying various types of faults in ball bearings. The proposed models were evaluated using the Case Western Reserve University (CWRU) bearing faults dataset, and the results showed a notable performance in terms of achieving high accuracy, recall, precision, and F1 score on the testing and validation sets. Compared to recent studies, the proposed AutoML models demonstrated superior performance, surpassing alternative approaches even when they utilized a larger number of features, thus highlighting the effectiveness of the proposed methodology. This research offers valuable insights for those interested in harnessing the potential of AutoML techniques in IIoT applications, with implications for industries such as manufacturing and energy. By automating the machine-learning process, AutoML models can help decrease the time and cost related to predictive maintenance, which is crucial for industries where unplanned downtime can lead to substantial financial losses. Full article
(This article belongs to the Special Issue Tools and Algorithms for Industrial System Production and Operation Management)
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18 pages, 4753 KiB  
Article
Posture and Map Restoration in SLAM Using Trajectory Information
by Weichen Wei, Mohammadali Ghafarian, Bijan Shirinzadeh, Ammar Al-Jodah and Rohan Nowell
Processes 2022, 10(8), 1433; https://doi.org/10.3390/pr10081433 - 22 Jul 2022
Cited by 1 | Viewed by 1860
Abstract
SLAM algorithms generally use the last system posture to estimate its current posture. Errors in the previous estimations can build up and cause significant drift accumulation. This accumulation of error leads to the bias of choosing accuracy over robustness. On the contrary, sensors [...] Read more.
SLAM algorithms generally use the last system posture to estimate its current posture. Errors in the previous estimations can build up and cause significant drift accumulation. This accumulation of error leads to the bias of choosing accuracy over robustness. On the contrary, sensors like GPS do not accumulate errors. But the noise distribution in the readings makes it difficult to apply in high-frequency SLAM systems. This paper presents an approach which uses the advantage of both tightly-coupled SLAM systems and highly robust absolute positioning systems to improve the robustness and accuracy of a SLAM process. The proposed method uses a spare reference trajectory frame to measure the trajectory of the targeted robotic system and use it to recover the system posture during the mapping process. This helps the robotic system to reduce its accumulated error and able the system to recover from major mapping failures. While the correction process happens whenever a gap is detected between the two trajectories, the external frame does not have to be always available. The correction process is only triggered when the spare trajectory sensors can communicate. Thus, it reduces the needed computational power and complexity. To further evaluate the proposed method, the algorithm was assessed in two field tests and a public dataset. We have demonstrated that the proposed algorithm has the ability to be adapted into different SLAM approaches with various map representations. To share our findings, the software constructed for this project is open-sourced on Github. Full article
(This article belongs to the Special Issue Tools and Algorithms for Industrial System Production and Operation Management)
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Review

Jump to: Research

21 pages, 1833 KiB  
Review
A Survey of DEA Window Analysis Applications
by Mohammed A. AlKhars, Ahmad H. Alnasser and Taqi AlFaraj
Processes 2022, 10(9), 1836; https://doi.org/10.3390/pr10091836 - 12 Sep 2022
Cited by 1 | Viewed by 2177
Abstract
This article aims to review, analyze, and classify the published research applications of the Data Envelopment Analysis (DEA) window analysis technique. The number of filtered articles included in the study is 109, retrieved from 79 journals in the web of science (WoS) database [...] Read more.
This article aims to review, analyze, and classify the published research applications of the Data Envelopment Analysis (DEA) window analysis technique. The number of filtered articles included in the study is 109, retrieved from 79 journals in the web of science (WoS) database during the period 1996–2019. The papers are classified into 15 application areas: energy and environment, transportation, banking, tourism, manufacturing, healthcare, power, agriculture, education, finance, petroleum, sport, communication, water, and miscellaneous. Moreover, we present descriptive statistics related to the growth of publications over time, the journals publishing the articles, keyword terms used, length of articles, and authorship analysis (including institutional and country affiliations). To the best of the authors knowledge, this is the first survey reviewing the literature of the DEA window analysis applications in the 15 areas mentioned in the paper. Full article
(This article belongs to the Special Issue Tools and Algorithms for Industrial System Production and Operation Management)
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