Intelligent Fault Detection and Diagnosis in Condition-Based Maintenance

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 541

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Interests: PHM; diagnostics; prognostics; uncertainty; reliability; condition-based maintenance; metrology; AI in maintenance

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Guest Editor Assistant
Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Interests: condition monitoring; prognostics and health management; wind turbine technology; wind turbine operations and maintenance; data-driven condition monitoring; deep learning applications in renewable energy systems

Special Issue Information

Dear colleagues,

The impressive progress made in the PHM field over the last decade is remarkable (increasing availability of data from sensors, AI algorithms, computing capabilities, etc.).

This paves the way for new approaches to address maintenance. All recent economic studies agree on the need to minimize Operations and Maintenance (O&M) costs to maintain the competitive level of the industry. The aim of this Special Issue is to present to the community in the field the most recent advances while emphasizing the industrial deployment of these approaches.

These advances now make it possible to obtain an almost instantaneous profile of a set of sensors embedded on the industrial asset. Some even suggest that today, ‘monitoring’ is a problem that has already been solved. However, several fundamental challenges and technological obstacles still exist, including the following: how can we quantify the uncertainty of a diagnosis? How can we transfer learning obtained from one asset to another that is similar but not identified? How can we evolve prognostic processes? How to make an accurate diagnosis from indirect measurements, and what is the inherent uncertainty in this operation? How can we maintain ‘interpretability’ with artificial intelligence algorithms?

This Special Issue seeks original research articles focusing on advances in all facets of diagnosis and prognosis. We welcome papers that offer new directions and research perspectives and especially that pose new challenges for our community. Contributions that demonstrate successful applications of the methods to complex equipment are particularly interesting.

We hope that this Special Issue will be useful and informative for researchers and practitioners in the relevant industrial fields. Research topics of interest in this Special Issue include, but are not limited to, the following:

  • Quantification of uncertainties in diagnostics and prognostics;
  • Risk management and decision support tools in predictive maintenance;
  • Fault monitoring, diagnostics and prognostics by indirect measurements;
  • Scalable and transferable diagnostics and prognostics from one type of equipment to another;
  • Strategies for the industrial deployment of new solutions;
  • Case studies from industrial applications (automotive, aeronautics, manufacturing, food, pharmaceutical, etc.).

Prof. Dr. Antoine Tahan
Guest Editor

Dr. Adaiton Oliveira Filho
Guest Editor Assistant

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

  • diagnostics
  • prognostics
  • PHM
  • maintenance
  • uncertainty
  • risk analysis

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Published Papers (1 paper)

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Research

21 pages, 4416 KiB  
Article
A Generic Modeling Method of Multi-Modal/Multi-Layer Digital Twins for the Remote Monitoring and Intelligent Maintenance of Industrial Equipment
by Maolin Yang, Yifan Cao, Siwei Shangguan, Xin Chen and Pingyu Jiang
Machines 2025, 13(6), 522; https://doi.org/10.3390/machines13060522 - 16 Jun 2025
Viewed by 314
Abstract
Digital twin (DT) is a useful tool for the remote monitoring, analyzing, controlling, etc. of industrial equipment in a harsh working environment unfriendly to human workers. Although much research has been devoted to DT modeling methods, there are still limitations. For example, (1) [...] Read more.
Digital twin (DT) is a useful tool for the remote monitoring, analyzing, controlling, etc. of industrial equipment in a harsh working environment unfriendly to human workers. Although much research has been devoted to DT modeling methods, there are still limitations. For example, (1) existing DT modeling methods are usually focused on specific types of equipment rather than being generally applicable to different types of equipment and requirements. (2) Existing DT models usually emphasize working condition monitoring and have relatively limited capability for modeling the operation and maintenance mechanism of the equipment for further decision making. (3) How to integrate artificial intelligence algorithms into DT models still requires further exploration. In this regard, a systematic and general DT modeling method is proposed for the remote monitoring and intelligent maintenance of industrial equipment. The DT model contains a multi-modal digital model, a multi-layer status model, and an intelligent interaction model driven by a kind of human-readable/computer-deployable event-state knowledge graph. Using the model, the dynamic workflows, working mechanisms, working status, workpiece logistics, monitoring data, and intelligent functions, etc., during the remote monitoring and maintenance of industrial equipment can be realized. The model was verified through three different DT modeling scenarios of a robot-based carbon block polishing processing line. Full article
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