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Applied Sciences
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Advances and Challenges in Reliability and Maintenance Engineering
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Advances and Challenges in Reliability and Maintenance Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 3393

Special Issue Editor

Prof. Dr. Benoit Iung

E-Mail Website
Guest Editor
CRAN (Nancy Research Centre for Automatic Control, UMR CNRS 7039), Lorraine University (UL, France), Vandœuvre-Lès-Nancy, France
Interests: maintenance engineering; PHM; predictive maintenance technologies; CPPS engineering; industry of the future

Special Issue Information

Dear Colleagues,

With the development of manufacturing technologies such as those promoted by Industry 4.0, reliability and maintenance engineering are increasing in importance due to rising amounts of equipment, systems, fleets of systems, machinery and infrastructure. This brings new challenges to production processes, cyber-physical systems and technology systems. Therefore, this Special Issue intends to present new ideas and solutions in the field of risk analysis, prognostics and health management, dependable cyber–physical systems, self-X in maintenance and dependability, as well as predictive maintenance.

Prof. Dr. Benoit Iung
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • reliability analysis
  • advanced maintenance engineering
  • reliability assessment
  • PHM processes (monitoring, diagnostics, prognostics, decision-making)
  • predictive maintenance
  • prescriptive maintenance)

Published Papers (5 papers)

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Research

23 pages, 7065 KiB  
Article
Study on Ring Deformation and Contact Characteristics of Thin-Walled Bearing for RV Reducer
by Yanshuang Wang and Fangzheng Liu
Appl. Sci. 2024, 14(9), 3741; https://doi.org/10.3390/app14093741 (registering DOI) - 27 Apr 2024
Viewed by 180
Abstract
The thin-walled rings of the RV reducer main bearings are prone to structural elastic deformation, which can significantly change the bearing mechanical characteristics. According to the actual assembly state of the RV reducer, the simulation model of the planetary frame–main bearings–pin gear housing [...] Read more.
The thin-walled rings of the RV reducer main bearings are prone to structural elastic deformation, which can significantly change the bearing mechanical characteristics. According to the actual assembly state of the RV reducer, the simulation model of the planetary frame–main bearings–pin gear housing is established considering the ring deformation. The model was used to calculate and comparatively analyze the ring deformation and contact characteristics of thin-walled bearings under rigid and flexible conditions, on the basis of which the mechanism of ring deformation was described, and the effects of load conditions, ring thickness and radial clearance on ring deformation, flexible contact characteristics, and ultimate carrying capacity were analyzed. The results show that the distribution of contact loads is the main factor affecting the ring deformation. The ring deformation can optimize the bearing contact characteristics, and the greater the deformation, the more pronounced the optimization effect. However, excessive ring deformation makes the contact ellipse more susceptible to truncation, which, in turn, reduces the ultimate carrying capacity. This study indicates a 38.2% decrease in the carrying capacity of the flexible ring model compared to that of the rigid ring model. In this paper, the effect of ring deformation on bearing mechanical characteristics is deeply discussed. The research results have important guiding significance for the structural optimization design of thin-walled bearings. Full article
(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)
19 pages, 4000 KiB  
Article
Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment
by Houxiang Wang, Haitao Liu and Songshi Shao
Appl. Sci. 2024, 14(7), 2901; https://doi.org/10.3390/app14072901 - 29 Mar 2024
Viewed by 316
Abstract
The investigation of the reliability of long-life equipment is typically hindered by the lack of experimental data, which makes accurate assessments challenging. To address this problem, a bootstrap method based on the improved RBF (radial basis function) neural network is proposed. This method [...] Read more.
The investigation of the reliability of long-life equipment is typically hindered by the lack of experimental data, which makes accurate assessments challenging. To address this problem, a bootstrap method based on the improved RBF (radial basis function) neural network is proposed. This method utilizes the exponential function to modify the conventional empirical distribution function and fit right-tailed data. In addition, it employs the RBF radial basis neural network to obtain the distribution characteristics of the original samples and then constructs the neighborhood function to generate the input network. The expanded sample is used to estimate the scale and shape parameters of the Weibull distribution and obtain the estimated value of the MTBF (mean time between failures). The bias correction method is then used to obtain the interval estimate for the MTBF. Subsequently, a simulation experiment is conducted based on the failure data of a CNC (computer numerical control) machine tool to verify the effect of this method. The results show that the accuracy of the MTBF point estimation and interval estimation obtained using the proposed method is superior to those of the original and conventional bootstrap methods, which is of major significance to engineering applications. Full article
(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)
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19 pages, 5592 KiB  
Article
A Bearing Fault Diagnosis Method Based on Improved Transfer Component Analysis and Deep Belief Network
by Dalin Li and Meiling Ma
Appl. Sci. 2024, 14(5), 1973; https://doi.org/10.3390/app14051973 - 28 Feb 2024
Viewed by 444
Abstract
Domain adaptation can handle data distribution in different domains and has been successfully applied to bearing fault diagnosis under variable working conditions. However, most of these methods ignore the influences of noise and data distribution discrepancy on marking pseudo labels. Additionally, most domain [...] Read more.
Domain adaptation can handle data distribution in different domains and has been successfully applied to bearing fault diagnosis under variable working conditions. However, most of these methods ignore the influences of noise and data distribution discrepancy on marking pseudo labels. Additionally, most domain adaptive methods require a large amount of data and training time. To overcome the aforementioned challenges, firstly, sample rejection and pseudo label correction using K-means (SRPLC-K-means) were developed and explored to filter the noisy samples and correct the pseudo labels to obtain pseudo labels with higher confidence. Furthermore, a bearing fault diagnosis method based on the improved transfer component analysis and deep belief network is proposed, which can achieve subdomain adaptation and improve the compactness of the samples, leading to a complete bearing fault diagnosis under variable working conditions that is faster and more accurate. Finally, the results of the comparative tests confirmed that the proposed method could boost the average accuracy of 0.73%, 0.99%, and 5.55% in the three tests than the state-of-the-art methods, respectively. Moreover, the comparison of the time required for a fault diagnosis using different methods shows that compared to the end-to-end models, the proposed method reduces the time required by 594.9 s and 1431.6 s, respectively. Full article
(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)
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15 pages, 566 KiB  
Article
Hi-RCA: A Hierarchy Anomaly Diagnosis Framework Based on Causality and Correlation Analysis
by Jingjing Yang, Yuchun Guo, Yishuai Chen and Yongxiang Zhao
Appl. Sci. 2023, 13(22), 12126; https://doi.org/10.3390/app132212126 - 08 Nov 2023
Viewed by 600
Abstract
Microservice architecture has been widely adopted by large-scale applications. Due to the huge amount of data and complex microservice dependency, it also poses new challenges in ensuring reliable performance and maintenance. Existing approaches still suffer from limitations of anomaly data, over-simplification of metric [...] Read more.
Microservice architecture has been widely adopted by large-scale applications. Due to the huge amount of data and complex microservice dependency, it also poses new challenges in ensuring reliable performance and maintenance. Existing approaches still suffer from limitations of anomaly data, over-simplification of metric relationships, and lack of diagnosing interpretability. To solve these issues, this paper builds a hierarchy root cause diagnosis framework, named Hi-RCA. We propose a global perspective to characterize different abnormal symptoms, which focuses on changes in metrics’ causation and correlation. We decompose the diagnosis task into two phases: anomalous microservice location and anomalous reason diagnosis. In the first phase, we use Kalman filtering to quantify microservice abnormality based on the estimation error. In the second phase, we use causation analysis to identify anomalous metrics and generate anomaly knowledge graphs; by correlation analysis, we construct an anomaly propagation graph and explain the anomaly symptoms via graph comparison. Our experimental evaluation on an open dataset shows that Hi-RCA can effectively locate root causes with 90% mean average precision, outperforming state-of-the-art methods. Full article
(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)
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22 pages, 5301 KiB  
Article
Assessment Possibilities of the Quality of Mining Equipment and of the Parts Subjected to Intense Wear
by Vlad Alexandru Florea, Mihaela Toderaș and Răzvan-Bogdan Itu
Appl. Sci. 2023, 13(6), 3740; https://doi.org/10.3390/app13063740 - 15 Mar 2023
Cited by 3 | Viewed by 1320
Abstract
The equipment in underground mines provides a continuous production flow, depending on the way their quality is preserved during their operation. The TR-7A scraper conveyer subassemblies, which function in the Jiu Valley coal basin and are subjected to abrasion wear, showed a high [...] Read more.
The equipment in underground mines provides a continuous production flow, depending on the way their quality is preserved during their operation. The TR-7A scraper conveyer subassemblies, which function in the Jiu Valley coal basin and are subjected to abrasion wear, showed a high failure frequency (chains, chain elevators, and driving and turning drums), as well as the hydraulic couplings and certain electric equipment of the same machinery. The data collected following the TR-7A scraper conveyer at work allowed the parameters to be determined that characterise the reliability and maintainability of the above-mentioned components, the failure modes, and their effects. Using calculation methods, the interpretation of the results has been facilitated, with a view to reducing maintenance costs and obtaining an 80% reliability for the components with the most failures, in the case of the TR-7A scraper conveyer. Full article
(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)
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