Evaluation of State of Health of Equipment for Predictive Maintenance and Circular Economy

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 711

Special Issue Editor


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Guest Editor
Computer Science, Université Grenoble Alpes, Grenoble, France
Interests: diagnostic; prognostic; maintenance; reliability; safety

Special Issue Information

Dear Colleagues,

Today, companies are trying to improve the reliability of their equipment by implementing technical solutions such as health monitoring and effective diagnostics. These improvements require the development of robust approaches. However, the increasing complexity of systems makes such developments difficult to implement. Furthermore, concerns surrounding environmental preservation and waste minimization require solutions to anticipate the shutdown of production due to breakdowns through predicting the state of degradation of production equipment.  Thus, more suitable maintenance strategies for their entire life cycle can be proposed. Within this framework, this Special Issue seeks recent research on themes dealing with unexpected events for complex systems, presenting new techniques and methodologies and discussing their strengths, weaknesses, and uncertainties to improve the performance of prediction techniques and the exploitation of operating data.

This Special Issue will focus on health assessment methodologies throughout the life cycle of machines. Topics of interest include:

  • Data-driven fault diagnosis techniques.
  • Advanced model-based fault diagnosis and fault-tolerant control techniques for complex industrial processes.
  • Intelligent fault diagnosis and fault-tolerant control techniques for safety-critical systems.
  • Real-time implementation and industrial applications.
  • Predictive maintenance including remanufacturing.
  • Maintenance and circular economy.
  • Management of obsolescence.

Prof. Dr. Zineb Simeu-Abazi
Guest Editor

Manuscript Submission Information

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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

  • degradation
  • diagnostic
  • prognostic
  • maintenance
  • state of health
  • monitoring
  • remanufacturing

Published Papers (1 paper)

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Research

14 pages, 2642 KiB  
Article
A Novel Method for Failure Mode and Effect Analysis Based on the Fermatean Fuzzy Set and Bonferroni Mean Operator
by Liangsheng Han, Mingyi Xia, Yang Yu and Shuai He
Machines 2024, 12(5), 332; https://doi.org/10.3390/machines12050332 - 13 May 2024
Viewed by 497
Abstract
Failure mode and effects analysis (FMEA) helps to identify the weak points in the processing, manufacturing, and assembly of products and plays an important role in improving product reliability. To address the shortcomings of the existing FMEA methods in terms of the uncertainty [...] Read more.
Failure mode and effects analysis (FMEA) helps to identify the weak points in the processing, manufacturing, and assembly of products and plays an important role in improving product reliability. To address the shortcomings of the existing FMEA methods in terms of the uncertainty treatment of information and not considering the weights and correlations between risk factors, we propose a new FMEA method. In this paper, the Fermatean fuzzy Z-number (FFZN) is proposed by fusing the Fermatean fuzzy number and Z-number. Extending it to the Bonferroni mean (BM) operator, the Fermatean fuzzy Z-number-weighted Bonferroni mean (FFZWBM) operator is proposed. A new FMEA method is proposed based on this operator. In order to overcome the factors not considered in the FMEA method, two new risk factors are proposed and added. The ability of experts to express fuzzy information is enhanced by introducing the FFS. The weights and correlations between the influencing factors can be handled by aggregating the evaluation information using the FFZWBM operator. Finally, the proposed method is applied to an arithmetic example and the accuracy of the proposed method is proved by teaming it with other methods. Full article
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