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System Reliability and Quality Management in Industrial Engineering
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System Reliability and Quality Management in Industrial Engineering

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 7816

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 decision supports for companies conducting the management of 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

Related Special Issue

Published Papers (9 papers)

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Research

23 pages, 991 KiB  
Article
A Bayesian Approach for Lifetime Modeling and Prediction with Multi-Type Group-Shared Missing Covariates
by Hao Zeng, Xuxue Sun, Kuo Wang, Yuxin Wen, Wujun Si and Mingyang Li
Mathematics 2024, 12(5), 740; https://doi.org/10.3390/math12050740 - 29 Feb 2024
Viewed by 653
Abstract
In the field of reliability engineering, covariate information shared among product units within a specific group (e.g., a manufacturing batch, an operating region), such as operating conditions and design settings, exerts substantial influence on product lifetime prediction. The covariates shared within each group [...] Read more.
In the field of reliability engineering, covariate information shared among product units within a specific group (e.g., a manufacturing batch, an operating region), such as operating conditions and design settings, exerts substantial influence on product lifetime prediction. The covariates shared within each group may be missing due to sensing limitations and data privacy issues. The missing covariates shared within the same group commonly encompass a variety of attribute types, such as discrete types, continuous types, or mixed types. Existing studies have mainly considered single-type missing covariates at the individual level, and they have failed to thoroughly investigate the influence of multi-type group-shared missing covariates. Ignoring the multi-type group-shared missing covariates may result in biased estimates and inaccurate predictions of product lifetime, subsequently leading to suboptimal maintenance decisions with increased costs. To account for the influence of the group-shared missing covariates with different structures, a new flexible lifetime model with multi-type group-shared latent heterogeneity is proposed. We further develop a Bayesian estimation algorithm with data augmentation that jointly quantifies the influence of both observed and multi-type group-shared missing covariates on lifetime prediction. A tripartite method is then developed to examine the existence, identify the correct type, and quantify the influence of group-shared missing covariates. To demonstrate the effectiveness of the proposed approach, a comprehensive simulation study is carried out. A real case study involving tensile testing of molding material units is conducted to validate the proposed approach and demonstrate its practical applicability. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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12 pages, 592 KiB  
Article
Optimal Mission Abort Decisions for Multi-Component Systems Considering Multiple Abort Criteria
by Xiaofei Chai, Boyu Chen and Xian Zhao
Mathematics 2023, 11(24), 4922; https://doi.org/10.3390/math11244922 - 11 Dec 2023
Viewed by 610
Abstract
This paper studies the optimal mission abort decisions for safety-critical mission-based systems with multiple components. The considered system operates in a random shock environment and is required to accomplish a mission during a fixed mission period. If the failure risk of the system [...] Read more.
This paper studies the optimal mission abort decisions for safety-critical mission-based systems with multiple components. The considered system operates in a random shock environment and is required to accomplish a mission during a fixed mission period. If the failure risk of the system is very high, the main mission can be aborted to avoid higher failure cost. The main contribution of this study lies in the design and optimization of mission abort policies for multi-component systems with multiple abort criteria. Moreover, multi-level transitions are considered in this study to characterize the different shock-resistance abilities for components in different states. Mission abort decisions are determined based on the number of components in either defective or failed state. The problem is formulated in the framework of the finite Markov chain imbedding method. We use the Monte-Carlo simulation method to derive the mission reliability and system survivability. Numerical studies and sensitivity analysis are presented to validate the obtained result. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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21 pages, 5942 KiB  
Article
Reliability Optimization of Hybrid Systems Driven by Constraint Importance Measure Considering Different Cost Functions
by Jiangbin Zhao, Mengtao Liang, Rongyu Tian, Zaoyan Zhang and Xiangang Cao
Mathematics 2023, 11(20), 4283; https://doi.org/10.3390/math11204283 - 13 Oct 2023
Cited by 1 | Viewed by 545
Abstract
The requirements of high reliability for hybrid systems are urgent for engineers to maximize the system reliability under the limited cost budget. The cost constraint importance measure (CIM) is an important tool to achieve the local optimal solution by considering the relationship between [...] Read more.
The requirements of high reliability for hybrid systems are urgent for engineers to maximize the system reliability under the limited cost budget. The cost constraint importance measure (CIM) is an important tool to achieve the local optimal solution by considering the relationship between constraint conditions and objective functions in the optimization problem. To better consider the contribution of the CIM, this paper considers three different cost function forms, including power type, trigonometric type, and exponential type. Combining the global search ability of the arithmetic optimization algorithm (AOA) with the local search ability of the CIM, a CIM-based arithmetic optimization algorithm (CIAOA) is developed to analyze the contribution of the CIM. Through the numerical experiments, the optimal system reliability and convergence generation of the CIAOA and AOA under different cost function forms are regarded as the indexes to analyze algorithm performance. The experimental results show that the average system reliability improvement percentages under power type, trigonometric type, and exponential cost constraint are 8.07%, 0.14%, and 0.53%, respectively, while the average convergence improvement percentages under three cost forms are 37.30%, 0.08%, and 1.66%, respectively. Therefore, the CIAOA performs the best under power cost constraints. Finally, a numerical example of a hybrid power vehicle system is introduced to analyze the contribution of the CIM under different cost functions by considering the reliability improvement rate in the optimal solution and the ranking of the CIM. The higher prioritization components in the two rankings are similar, which shows that the component with higher a CIM is selected to improve its reliability. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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18 pages, 1731 KiB  
Article
Joint Optimization of Maintenance and Spare Parts Inventory Strategies for Emergency Engineering Equipment Considering Demand Priorities
by Xiaoyue Wang, Jingxuan Wang, Ru Ning and Xi Chen
Mathematics 2023, 11(17), 3688; https://doi.org/10.3390/math11173688 - 27 Aug 2023
Cited by 3 | Viewed by 962
Abstract
To respond to emergencies in a timely manner, emergency engineering equipment has been an important tool to implement emergency strategies. However, random failures of the equipment may occur during operation. Therefore, appropriate maintenance and spare parts inventory strategies are crucial to ensure the [...] Read more.
To respond to emergencies in a timely manner, emergency engineering equipment has been an important tool to implement emergency strategies. However, random failures of the equipment may occur during operation. Therefore, appropriate maintenance and spare parts inventory strategies are crucial to ensure the smooth operation of the equipment. Furthermore, the urgency degree of emergencies varies in practice. Nevertheless, existing studies rarely consider the impact of urgency degree and demand priorities on the service order of the equipment. To bridge the research gaps, this paper establishes a joint optimization model of maintenance and spare parts inventory strategies for emergency engineering equipment considering demand priorities. The proposed model includes two types of emergency engineering equipment with different service rates. The more urgent demand can be fulfilled by the equipment with a higher priority. Corrective maintenance and spare parts inventory policies are simultaneously performed for the equipment. The Markov process imbedding method is utilized to derive the probabilistic indexes of the system. To maximize the system availability, the number of maintenance engineers and the spare parts inventory strategy is optimized via the construction of the joint optimization model. The optimal solution for the optimization problem is obtained using the branch-and-bound method. Finally, this study presents practical examples to verify the effectiveness of the model and methods. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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16 pages, 4071 KiB  
Article
A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes
by Cang Wu, Huijuan Hou, Chunli Lei, Pan Zhang and Yongjun Du
Mathematics 2023, 11(16), 3589; https://doi.org/10.3390/math11163589 - 19 Aug 2023
Viewed by 676
Abstract
Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot [...] Read more.
Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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22 pages, 2199 KiB  
Article
Random Warranty and Replacement Models Customizing from the Perspective of Heterogeneity
by Lijun Shang, Baoliang Liu, Kaiye Gao and Li Yang
Mathematics 2023, 11(15), 3330; https://doi.org/10.3390/math11153330 - 29 Jul 2023
Viewed by 610
Abstract
Driven by the wide application of industrial software integrated with digital technologies, the real information of task cycles for some products in the real world can be monitored in real time and transmitted to the management center. Monitored task cycles hide consumers’ usage [...] Read more.
Driven by the wide application of industrial software integrated with digital technologies, the real information of task cycles for some products in the real world can be monitored in real time and transmitted to the management center. Monitored task cycles hide consumers’ usage characteristics, which are signals of the products’ usage heterogeneity because they vary from one consumer to another consumer. By classifying monitored task cycles into different categories, this paper customizes two random maintenance models to ensure the life cycle reliability of the product with monitored task cycles on the basis of usage categories. The first model is customized using usage categories, the key objective of which is, from the perspective of heterogeneity, to ensure warranty-stage reliability. In view of using minimal repair service, the first model is named a random free repair warranty with heterogeneity (RFRW-H), which is modeled from the viewpoints of cost and time measures. By calculating the limits of cost and time measures, some specific cases are presented to model other warranties. The second model is customized using the same usage categories, which aims to ensure post-warranty-stage reliability. In view of using each of ‘whichever occurs first/last’, the second model is named a customized random periodic replacement first (CRPRF) model or a customized random periodic replacement last (CRPRL) model, respectively, which are modeled from the viewpoint of the cost rate function. By calculating the limits of the cost rate function, the cost rate functions of other maintenance models are obtained. Finally, from the numerical viewpoint, some of the features of the customized models are mined, and the performances are compared. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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19 pages, 656 KiB  
Article
Joint Optimization of Condition-Based Maintenance and Performance Control for Linear Multi-State Consecutively Connected Systems
by Jun Wang, Yuyang Wang and Yuqiang Fu
Mathematics 2023, 11(12), 2724; https://doi.org/10.3390/math11122724 - 15 Jun 2023
Cited by 3 | Viewed by 957
Abstract
Industrial systems such as signal relay stations and oil pipeline systems can be modeled as linear multi-state consecutively connected systems, which comprise sequentially ordered elements and fail if the first and the final elements are not connected. The performance level of each element [...] Read more.
Industrial systems such as signal relay stations and oil pipeline systems can be modeled as linear multi-state consecutively connected systems, which comprise sequentially ordered elements and fail if the first and the final elements are not connected. The performance level of each element is controllable, which determines how many elements an element can connect and affects its degradation rate. Accumulated degradation can cause element failure, which may lead to costly system failure. This paper aims to minimize long-term maintenance-related costs, including system failure costs. We provide optimal maintenance planning and performance control for every system degradation state through Markov decision process modeling and a dynamic programming algorithm. Load-sharing, restricted maintenance capacity, maintenance setup costs, and the structural characteristics of the system are considered in the model, all of which influence the optimal maintenance and performance control policy. Regarding degradation management, reducing the difference in degradation levels between elements, e.g., replacing more-degraded elements first, can be cost-effective. However, increasing the difference in degradation by maintenance or performance control can also lower maintenance-related costs in specific situations, which is discussed in numerical experiments. We also illustrate structural insights regarding the proposed model, including sensitivity analyses of maintenance capacity, setup costs, and the difference between preventive and corrective replacement costs. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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20 pages, 4047 KiB  
Article
Grouping Maintenance Policy for Improving Reliability of Wind Turbine Systems Considering Variable Cost
by Hongyan Dui, Yulu Zhang and Yun-An Zhang
Mathematics 2023, 11(8), 1954; https://doi.org/10.3390/math11081954 - 20 Apr 2023
Cited by 2 | Viewed by 1088
Abstract
Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are [...] Read more.
Wind farms have gained wide attention due to unlimited resources and clean energy. Considering that wind turbine systems are always in harsh conditions, subsystem failures could reduce the reliability of wind turbine systems. At present, the maintenance behaviors for wind turbine systems are various (e.g., corrective maintenance, preventive maintenance) when reliability is reduced below the threshold. Considering the maintenance cost and downtime, it is impossible to repair each component in a timely manner. One of the key problems is dividing components into maintenance groups to improve maintenance efficiency. In this paper, a grouping maintenance policy considering the variable cost (GMP-VC) is proposed to improve direct-drive permanent magnet (DPM) turbine systems. Grouping modes are proposed to fully consider the stated transition probability of turbine components and the variable cost of turbine systems. A maintenance model is formulated to select components as members of the group based on a RIM-VC index. An instance is given to verify the proposed GMP-VC method. The result indicates that the proposed maintenance policy may save maintenance costs over baseline plans. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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17 pages, 315 KiB  
Article
Optimal Stopping and Loading Rules Considering Multiple Attempts and Task Success Criteria
by Yaguang Wu
Mathematics 2023, 11(4), 1065; https://doi.org/10.3390/math11041065 - 20 Feb 2023
Cited by 1 | Viewed by 817
Abstract
Numerous engineering systems gradually deteriorate due to internal stress caused by the working load. The system deterioration process is directly related to the workload, providing opportunities for decision-makers to manage system deterioration by modifying the workload. As one of the most effective ways [...] Read more.
Numerous engineering systems gradually deteriorate due to internal stress caused by the working load. The system deterioration process is directly related to the workload, providing opportunities for decision-makers to manage system deterioration by modifying the workload. As one of the most effective ways to control system malfunction risk, mission stopping has been extensively studied. Most existing research on mission stopping ignores the effect of working loads on the internal deterioration of safety-critical systems. The purpose of this work is to examine the optimal joint loading and stopping rules for systems subject to internal degradation under two types of mission success requirements (MSR). The problem is formulated using the recursive algorithm to minimize the expected cost over the mission. Mission reliability and system safety are assessed, and the optimal loading and stopping rules are investigated. The established models are illustrated by practical examples, and comprehensive policy comparison and parameter sensitivity analysis on the allowable mission time, mission duration and the number of mission tries are conducted. Our findings indicate that dynamic load level modification has a substantial effect on system deterioration and predicted long-term costs. For the purpose of decision-making, several managerial implications for the joint development of load adjustment and abort implementation are obtained. Full article
(This article belongs to the Special Issue System Reliability and Quality Management in Industrial Engineering)
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