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Machines
Special Issues
Artificial Intelligence for Cyber-Enabled Industrial Systems
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Artificial Intelligence for Cyber-Enabled Industrial Systems

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (31 August 2018) | Viewed by 7587

Special Issue Editors

Prof. Dr. Jay Lee

E-Mail Website
Guest Editor
NSF Industry/University Cooperative Research Center for Intelligent Maintenance Systems (IMS), Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
Interests: predictive big data analytics; cyber physical systems; prognostics and health management (PHM); Industry 4.0
Dr. Hossein Davari

E-Mail Website
Guest Editor
NSF Industry/University Cooperative Research Center for Intelligent Maintenance Systems (IMS), Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, OH 45221, USA
Interests: prognostics and health management; signal processing; machine learning; intelligent systems; cyber-physical systems

Special Issue Information

Dear Colleagues,

Advances in sensors, data collection, and communication technologies have paved the way for the collection and accumulation of large amounts of data in manufacturing, energy, transportation, health care, finance and more. Additionally, the availability and affordability of computational platforms provide a tremendous opportunity to fuse, synchronize and analyze the multi-dimensional data and extract actionable information that can bring transparency and improve the efficiency, productivity and availability of industrial machinery.

This Special Issue aims to provide a platform for multidisciplinary articles that investigate the state-of-the-art data analytics and Artificial Intelligence (AI) techniques in industrial applications with emphasis on practical aspects and implementation.

Relevant topics for this Special Issue include, but are not limited to:

  • Transformative technologies for realizing Industry 4.0
  • Systematic methodologies for industrial big data analytics
  • Integration of physics-based and data-driven models for realization of Cyber-Physical Systems
  • Intelligent decision support tools
  • Sensor-rich and sensor-less methods for prognostics and health management
Prof. Jay Lee
Dr. Hossein Davari
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. Machines 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

  • Cyber-Physical Systems
  • Industrial Big Data Analytics
  • Industrial Artificial Intelligence
  • Prognostics and Health Management

Published Papers (2 papers)

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Research

16 pages, 2610 KiB  
Article
Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets
by Keren Wang and Dragan Djurdjanovic
Machines 2018, 6(4), 55; https://doi.org/10.3390/machines6040055 - 01 Nov 2018
Cited by 9 | Viewed by 3897
Abstract
Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics [...] Read more.
Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics of maintenance resources. The joint decision-making problem becomes particularly challenging if one considers multiple options for preventive maintenance operations and multiple delivery methods for the necessary spare parts. In this paper, we propose an integrated decision-making policy that jointly considers scheduling of preventive maintenance for geographically dispersed multi-part assets, managing inventories for spare parts being stocked in maintenance facilities, and choosing the proper delivery options for the spare part inventory flows. A discrete-event, simulation-based meta-heuristic was used to optimize the expected operating costs, which reward the availability of assets and penalizes the consumption of maintenance/logistic resources. The benefits of joint decision-making and the incorporation of multiple options for maintenance and logistic operations into the decision-making framework are illustrated through a series of simulations. Additionally, sensitivity studies were conducted through a design-of-experiment (DOE)-based analysis of simulation results. In summary, considerations of concurrent optimization of maintenance schedules and spare part logistic operations in an environment in which multiple maintenance and transpiration options are available are a major contribution of this paper. This large optimization problem was solved through a novel simulation-based meta-heuristic optimization, and the benefits of such a joint optimization are studied via a unique and novel DOE-based sensitivity analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cyber-Enabled Industrial Systems)
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21 pages, 4544 KiB  
Article
Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems
by Edoardo Storti, Laura Cattaneo, Adalberto Polenghi and Luca Fumagalli
Machines 2018, 6(4), 45; https://doi.org/10.3390/machines6040045 - 02 Oct 2018
Cited by 2 | Viewed by 3286
Abstract
The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is [...] Read more.
The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is the absence of a well-established and standardized Industrial Big Data Analytics procedure, in particular for the application within the assembly systems. This work aims to develop a customized Knowledge Discovery in Databases (KDD) procedure for its application within the assembly department of Bosch VHIT S.p.A., active in the automotive industry. The work is focused on the data mining phase of the KDD process, where ARIMA method is used. Various applications to different lines of the assembly systems show the effectiveness of the customized KDD for the exploitation of production databases for the company, and for the spread of such a methodology to other companies too. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cyber-Enabled Industrial Systems)
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