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State of the Art in the Field of Machine and...
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State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 4312

Special Issue Editor

Dr. Pascal Vrignat

E-Mail Website
Guest Editor
PRISME, University of Orléans, EA 4229, 45072 Orléans, France
Interests: remaining useful life estimation

Special Issue Information

Dear Colleagues,

The increasing complexity of industrial processes, the continued goal of achieving higher profits, and increasingly demanding production constraints call for the implementation of a proactive and sustainable maintenance policy. Adapting maintenance policies that integrate the concepts and obligations of so-called sustainable development is a real challenge for companies. This can concern proactive maintenance operations aimed at providing balance between the social, environmental, and economic dimensions. Such a policy requires the introduction of substantial upstream analysis and the establishment of tools to validate process performance continuity. Consequently, and in an Industry 4.0 context, being able to anticipate a system breakdown based on its estimated degradation, while proposing a time window for a maintenance intervention has become essential. The aim is to avoid a failure with a particularly significant impact. The approaches and methods of Prognostics and Health Management (P.H.M) provide many answers.

This Special Issue looks at the current methods and tools available to meet the needs of P.H.M.

Dr. Pascal Vrignat
Guest Editor

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

  • sustainable manufacturing
  • maintenance policies
  • prognostics and health management
  • industry 4.0
  • prognosis
  • diagnosis
  • case studies

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

15 pages, 6899 KiB  
Article
Influence of Potting Radius on the Structural Performance and Failure Mechanism of Inserts in Sandwich Structures
by Filippos Filippou and Alexis Τ. Kermanidis
Machines 2025, 13(1), 34; https://doi.org/10.3390/machines13010034 - 7 Jan 2025
Viewed by 438
Abstract
In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the [...] Read more.
In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the pull out, load-carrying capacity, and failure mechanisms of the inserts. The methods of stiffness degradation and acoustic emissions (AE) were employed for structural health monitoring to capture real-time data on failure progression, including core buckling, core rupture, and skin delamination. The results indicated that increasing the potting radius significantly altered the failure modes and critical failure load of the insert system. A critical potting radius was identified where maximum stiffness was achieved. Beyond this point, insert fracture became the dominant failure mode, with minimal damage to the surrounding core and CFRP skins. Larger potting radii also led to reduced displacement at failure, increased ultimate loads, and elevated stiffness, which were maintained until sudden structural failure. Through detailed isolation and observation of each failure event and with the use of AE data, precise identification of system damage in real time was allowed, offering insights into the progression and causes of failure. Full article
(This article belongs to the Special Issue State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures)
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28 pages, 1585 KiB  
Article
Towards the Best Solution for Complex System Reliability: Can Statistics Outperform Machine Learning?
by María Luz Gámiz, Fernando Navas-Gómez, Rafael Adolfo Nozal Cañadas and Rocío Raya-Miranda
Machines 2024, 12(12), 909; https://doi.org/10.3390/machines12120909 - 11 Dec 2024
Viewed by 654
Abstract
Studying the reliability of complex systems using machine learning techniques involves facing a series of technical and practical challenges, ranging from the intrinsic nature of the system and data to the difficulties in modeling and effectively deploying models in real-world scenarios. This study [...] Read more.
Studying the reliability of complex systems using machine learning techniques involves facing a series of technical and practical challenges, ranging from the intrinsic nature of the system and data to the difficulties in modeling and effectively deploying models in real-world scenarios. This study compares the effectiveness of classical statistical techniques and machine learning methods for improving complex system analysis in reliability assessments. Our goal is to show that in many practical applications, traditional statistical algorithms frequently produce more accurate and interpretable results compared with black-box machine learning methods. The evaluation is conducted using both real-world data and simulated scenarios. We report the results obtained from statistical modeling algorithms, as well as from machine learning methods including neural networks, K-nearest neighbors, and random forests. Full article
(This article belongs to the Special Issue State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures)
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13 pages, 819 KiB  
Article
Optimal Inspection and Maintenance Policy: Integrating a Continuous-Time Markov Chain into a Homing Problem
by Mario Lefebvre and Roozbeh Yaghoubi
Machines 2024, 12(11), 795; https://doi.org/10.3390/machines12110795 - 10 Nov 2024
Viewed by 669
Abstract
The state of a machine is modeled as a controlled continuous-time Markov chain. Moreover, the machine is being serviced at random times. The aim is to maximize the time until the machine must be repaired, while taking the maintenance costs into account. The [...] Read more.
The state of a machine is modeled as a controlled continuous-time Markov chain. Moreover, the machine is being serviced at random times. The aim is to maximize the time until the machine must be repaired, while taking the maintenance costs into account. The dynamic programming equation satisfied by the value function is derived, enabling optimal decision-making regarding inspection rates, and special problems are solved explicitly. This approach minimizes direct maintenance costs along with potential failure expenses, establishing a robust methodology for determining inspection frequencies in reliability-centered maintenance. The results contribute to the advancement of maintenance strategies and provide explicit solutions for particular cases, offering ideas for application in reliability engineering. Full article
(This article belongs to the Special Issue State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures)
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15 pages, 4244 KiB  
Article
Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection
by Kun Wang, Yukun Huang, Baoqiang Zhang, Huageng Luo, Xiang Yu, Dawei Chen and Zhiqiang Zhang
Machines 2024, 12(2), 101; https://doi.org/10.3390/machines12020101 - 1 Feb 2024
Cited by 1 | Viewed by 1490
Abstract
Synchronous analysis is one of the most effective and practical techniques in rotating machinery diagnostics, especially in cases with variable speed operations. A modern analog-to-digital convertor (ADC) usually digitizes an analog signal to an equal time interval data series. Synchronous resampling converts the [...] Read more.
Synchronous analysis is one of the most effective and practical techniques in rotating machinery diagnostics, especially in cases with variable speed operations. A modern analog-to-digital convertor (ADC) usually digitizes an analog signal to an equal time interval data series. Synchronous resampling converts the data series from an equal time interval data series to an equal shaft rotation angle interval data series. This conversion is usually achieved in the digital domain with the aid of shaft speed information, through either direct measurement or identification from a measured vibration signal, which is a time-consuming process. In order to improve the computational efficiency as well as the data processing accuracy, in this paper, a fast synchronous time-point calculation method based on an inverse function interpolation procedure is proposed. By identifying the inverse function of the instantaneous phase with respect to time, the calculation process of synchronous time points is optimized, which results in improved calculation efficiency and accuracy. These advantages are demonstrated by numerical simulations as well as experimental verifications. The numerical simulation results show that the proposed method can improve calculation speed by about five times. The synchronous analysis based on the proposed method was applied to a bearing fault detection in a high-speed rail carriage, which demonstrated the advantages of the proposed algorithm in improving the signal-to-noise ratio (SNR) for bearing damage feature extraction. Full article
(This article belongs to the Special Issue State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures)
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