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System Reliability and Quality Management in Industrial Engineering, 2nd Edition
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System Reliability and Quality Management in Industrial Engineering, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2706

Special Issue Editors

Prof. Dr. Xian Zhao

E-Mail Website
Guest Editor
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
Interests: system reliability and management; quality management
Special Issues, Collections and Topics in MDPI journals
Dr. Qingan Qiu

E-Mail Website
Guest Editor
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
Interests: reliability modelling; stochastic operations research; power system reliability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are invited to contribute to this Special Issue, entitled “System Reliability and Quality Management in Industrial Engineering”. The main scope of this issue focuses on advances in the reliability modelling and analysis of engineering systems, maintenance and operation optimization problems of engineering systems, theory and methods of quality management, and applications of stochastic models in industrial engineering. Research on the reliability modelling and analysis of systems has been a hot topic in the field of industrial engineering. The reliability of engineering systems has become the main concern of customers and companies. System reliability has great importance because it determines the realization result of system functions. Diverse maintenance and operation policies are designed and optimized for engineering systems in practice, which can enhance the system’s reliability, prolong the system’s lifetime, and so on. Furthermore, quality has become a major business strategy for increasing productivity and gaining a competitive advantage. The theory and methods of quality management play a vital role in guiding companies to achieve their business goals. To conclude, the research achievements of system reliability and quality management in industrial engineering can provide support for decisions that companies make when managing system reliability and quality.

Prof. Dr. Xian Zhao
Dr. Qingan Qiu
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. Mathematics 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 2600 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

  • system reliability
  • reliability modeling
  • reliability analysis
  • maintenance policy
  • operation policy
  • quality management
  • quality control
  • stochastic models
  • Markov and semi-Markov models

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Related Special Issue

Published Papers (2 papers)

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Research

21 pages, 3864 KiB  
Article
Discrete Random Renewable Replacements after the Expiration of Collaborative Preventive Maintenance Warranty
by Hui Chen, Jie Chen, Yangyang Lai, Xiaoqi Yu, Lijun Shang, Rui Peng and Baoliang Liu
Mathematics 2024, 12(18), 2845; https://doi.org/10.3390/math12182845 - 13 Sep 2024
Viewed by 729
Abstract
With advanced digital technologies as the key support, many scholars and researchers have proposed various random warranty models by integrating mission cycles into the warranty stage. However, these existing warranty models are designed only from the manufacturer’s subjective perspective, ignoring certain consumer requirements. [...] Read more.
With advanced digital technologies as the key support, many scholars and researchers have proposed various random warranty models by integrating mission cycles into the warranty stage. However, these existing warranty models are designed only from the manufacturer’s subjective perspective, ignoring certain consumer requirements. For instance, they overlook a wide range of warranty coverage, the pursuit of reliability improvement rather than mere minimal repair, and the need to limit the delay in repair. To address these consumer requirements, this paper proposes a novel random collaborative preventive maintenance warranty with repair-time threshold (RCPMW-RTT). This model incorporates terms that are jointly designed by manufacturers and consumers to meet specific consumer needs, thereby overcoming the limitations of existing warranty models. The introduction of a repair-time threshold aims to limit the time delay in repairing failures and to compensate for any losses incurred by consumers. Using probability theory, the RCPMW-RTT is evaluated in terms of cost and time, and relevant variants are derived by analyzing key parameters. As an exemplary representation of the RCPMW-RTT, two random replacement policies named the discrete random renewable back replacement (DRRBR) and the discrete random renewable front replacement (DRRFR) are proposed and modelled to ensure reliability after the expiration of the RCPMW-RTT. In both policies, product replacement is triggered either by the occurrence of the first extreme mission cycle or by reaching the limit on the number of non-extreme mission cycles, whichever comes first. Probability theory is used to present cost rates for both policies in order to determine optimal values for decision variables. Finally, numerical analysis is performed on the RCPMW-RTT to reveal hidden variation tendencies and mechanisms; numerical analysis is also performed on the DRRBR and the DRRFR. The numerical results show that the proposed random replacement policies are feasible and unique; the replacement time within the post-warranty coverage increases as the maintenance quality improves and the cost rate can be reduced by setting a smaller repair-time threshold. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering, 2nd Edition)
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22 pages, 3812 KiB  
Article
Enhancing Fault Diagnosis in Mechanical Systems with Graph Neural Networks Addressing Class Imbalance
by Wenhao Lu, Wei Wang, Xuefei Qin and Zhiqiang Cai
Mathematics 2024, 12(13), 2064; https://doi.org/10.3390/math12132064 - 1 Jul 2024
Cited by 1 | Viewed by 1557
Abstract
Recent advancements in intelligent diagnosis rely heavily on data-driven methods. However, these methods often encounter challenges in adequately addressing class imbalances in the context of the fault diagnosis of mechanical systems. This paper proposes the MeanRadius-SMOTE graph neural network (MRS-GNN), a novel framework [...] Read more.
Recent advancements in intelligent diagnosis rely heavily on data-driven methods. However, these methods often encounter challenges in adequately addressing class imbalances in the context of the fault diagnosis of mechanical systems. This paper proposes the MeanRadius-SMOTE graph neural network (MRS-GNN), a novel framework designed to synthesize node representations in GNNs to effectively mitigate this issue. Through integrating the MeanRadius-SMOTE oversampling technique into the GNN architecture, the MRS-GNN demonstrates an enhanced capability to learn from under-represented classes while preserving the intrinsic connectivity patterns of the graph data. Comprehensive testing on various datasets demonstrates the superiority of the MRS-GNN over traditional methods in terms of classification accuracy and handling class imbalances. The experimental results on three publicly available fault diagnosis datasets show that the MRS-GNN improves the classification accuracy by 18 percentage points compared to some popular methods. Furthermore, the MRS-GNN exhibits a higher robustness in extreme imbalance scenarios, achieving an AUC-ROC value of 0.904 when the imbalance rate is 0.4. This framework not only enhances the fault diagnosis accuracy but also offers a scalable solution applicable to diverse mechanical and complex systems, demonstrating its utility and adaptability in various operating environments and fault conditions. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering, 2nd Edition)
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