Applied Artificial Intelligence for Data-Driven Predicative Maintenance of Equipment

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 1266

Special Issue Editors


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Guest Editor
School of Computing Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: artificial intelligence; machine learning; cyber security; intrusion detection systems; information security
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Guest Editor
Department of Computer Science & Engineering, Lahore Garrison University, Lahore, Pakistan
Interests: applied artificial intelligence and fault detection; insulation and recovery

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Guest Editor
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK
Interests: artificial intelligence; affective computing; Internet of Things
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Special Issue Information

Dear Colleagues,

In the modern era, sophisticated and complex systems are part of modern industry—for instance, heavy machines, engines, electronic circuits and other complex structures. The failure of industrial equipment leads to unwanted downtime, production loss, economic damage, and it also put the safety of the workers at risk. These issues can be mitigated if the maintenance of such assets is performed on a timely basis. This is only possible if well before the failure of a component, the issues related to it are identified. In general, the maintenance of an object can be defined as servicing, functional checks, and repairing or replacing required components, machinery, artificial infrastructures, and supporting utilities in industrial, residential, business installations. In other words, the maintenance process can be defined as maintaining the equipment by troubleshooting problems either manually or through computerized diagnostic tools. Nowadays, due to the availability of different types of sensors, data recording has become an easy task. So, huge amounts of data related to different types of mechanical, electronic, and other complex systems are already available in the form of public repositories. Moreover, if desired data are not publicly available, they can be collected through the deployment of an appropriate sensory system and following standard data collection techniques. Later on, these data can be analyzed using different artificial intelligence techniques to develop the intelligent data-driven predictive maintenance of equipment. The devised strategies may consist of simple statistical analysis of the data to perform a cost–benefit analysis and forecasting or address the technical issue, for instance, fault detection and diagnosis of equipment. 

This Special Issue intends to present original research papers with high quality and novelty as well as review papers on “Applied Artificial Intelligence for Data-Driven Predictive Maintenance of Equipment”.

Topics of interest include, but are not limited to:

  • Artificial intelligence, machine learning and deep learning for predictive maintenance;
  • Data analytics for system operation and control;
  • Multimodal data analytics and fusion;
  • Distributed data mining;
  • Cloud computing for data analytics and predictive maintenance;
  • Data analytics for a resource demand forecasting;
  • Data collection, visualization, statistical analysis, storage, and information management in industrial systems;
  • Fault detection and diagnosis for energy systems.

Dr. Sana Ullah Jan
Dr. Muhammad Sohaib
Prof. Dr. Naeem Ramzan
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

  • machine diagnostics and prognostics (condition monitoring)
  • applications of automation
  • computer engineering
  • mechanical systems, machines and related components

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Published Papers

There is no accepted submissions to this special issue at this moment.
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