Article
Computer Science, Information Systems
Guoqing Bai, Yuanying Chi, Kaiye Gao, Rui Peng
Summary: This study extends the reliability evaluation model of a multi-state system by considering both the minimum and maximum requirements of elements and estimates the reliability using the Universal Generating Function technique. Additionally, the genetic operator is used to determine the optimal location of components with maximum reliability.
Article
Mathematics, Interdisciplinary Applications
Zheng Li, Jinlei Qin
Summary: Multistate systems have become a general trend in complex industrial products and systems, with fault-tolerant technology playing a key role in improving reliability. Imperfect coverage failure in a work-sharing group can reduce reliability, but a method using universal generating function and matrix-based algorithm can assess and enhance the reliability of multistate systems. Sensitivity analysis helps identify which work-sharing group should be prioritized for elimination under resource limitations.
Article
Mathematics, Applied
Jean-Paul Chehab, Vivien Desveaux, Marouan Handa
Summary: This study focuses on optimization problems in energy distribution systems with storage, considering a simplified network topology around four nodes: the load aggregator, the external grid, the consumption, and the storage. By solving two optimization problems, mathematical models are established and a new method based on a sliding window algorithm is proposed to reduce computational time and enable real-time simulations.
Article
Computer Science, Hardware & Architecture
Hui Xiao, Kunxiang Yi, Rui Peng, Gang Kou
Summary: Existing research has focused on improving the reliability of distributed computing systems, without considering the performance sharing mechanism. This study proposes a reliability model for evaluating distributed computing systems with performance sharing, and formulates an optimization model for deriving the optimal performance sharing policy.
IEEE TRANSACTIONS ON RELIABILITY
(2022)
Article
Engineering, Industrial
Hui Xiao, Kunxiang Yi, Haitao Liu, Gang Kou
Summary: The study focuses on a two-dimensional sliding window system consisting of multi-state components, evaluating system reliability through extending the universal generating function technique. Further optimization of component maintenance and allocation is explored to enhance system availability. The proposed model and algorithm are illustrated through a case study on a collaborative robot supported matrix automotive assembly system.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Industrial
Congshan Wu, Xian Zhao, Xiaoyue Wang, Siqi Wang
Summary: This paper establishes a performance-based balanced system with common bus performance sharing, where components are connected by a common bus and performance can be redistributed to maintain balance. A Markov process is employed to describe performance variation and analytical solutions for system reliability are obtained using generating function technique. A specific analysis on battery balancing of lithium battery pack demonstrates the effectiveness of the proposed model and method.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Computer Science, Theory & Methods
Siqi Qiu, Xinguo Ming
Summary: This paper studies fuzzy multi-state systems with performance sharing between adjacent units, proposing a method based on fuzzy universal generating function to evaluate the fuzzy reliability of systems considering parametric uncertainty. The innovation lies in two aspects: surplus performance can only be shared with adjacent units experiencing deficiency, and parametric uncertainty related to state probabilities is considered.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Jinyu Zhou, Liyang Xie, Wenqin Han
Summary: This paper introduces a backward recursive algorithm based on the universal generating function for structural reliability calculation with characteristics of multivariable, non-normality, small failure probability, and nonlinearity. Compared with traditional methods, the proposed method demonstrates higher accuracy and computational efficiency, providing a new approach for reliability calculation considering complex performance functions.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Computer Science, Artificial Intelligence
Hu Peng, Jiayao Qian, Fanrong Kong, Debin Fan, Peng Shao, Zhijian Wu
Summary: This paper proposes an enhancing firefly algorithm with sliding window (SWFA) to improve the original firefly algorithm (FA). SWFA incorporates the sliding window mechanism, reverse learning, and an adaptive step adjustment strategy to enhance the algorithm's performance.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Physics, Multidisciplinary
Yanhao Wang, Francesco Fabbri, Michael Mathioudakis, Jia Li
Summary: This paper studies diversity maximization with fairness constraints in streaming and sliding-window models. By designing efficient approximation algorithms, the problem of fair max-min diversity maximization in data streams and sliding-window models is addressed. Experimental results show that our algorithms can run several orders of magnitude faster than existing offline algorithms in streaming and sliding-window settings, while providing comparable solution quality.
Article
Physics, Multidisciplinary
Lei Zhou, Bolun Chen, Hu Liu, Liuyang Wang
Summary: In this paper, a personalized sliding window is designed for different users by combining timing information and network topology information. The information sequence of each user in the sliding window is extracted and user similarity is obtained through sequence alignment. The results show that our method outperforms traditional algorithms in recommendation accuracy, popularity, and diversity.
Article
Engineering, Multidisciplinary
Tianzi Tian, Jun Yang, Ning Wang
Summary: Two-level balanced systems with common bus performance sharing (TLBS-CBPS), represented by power battery systems, play a crucial role in various industries. However, the uncertainty of component performance due to measurement limitations and the transmission loss during performance redistribution make the reliability evaluation of TLBS-CBPS challenging. To address these issues, a reliability evaluation method for TLBS-CBPS is proposed by considering two-level balance, epistemic uncertainty, and transmission loss.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2023)
Article
Engineering, Industrial
Jingkui Li, Yuze Lu, Xiaona Liu, Xiuhong Jiang
Summary: This paper presents a new operator for simulating cold-standby systems and proposes a reliability analysis model for PMS with sequential operation and time continuation based on the GO-FLOW methodology and universal generating function (UGF). The main advantage of the proposed model is that it avoids the need to deal with shared signals in system reliability calculations. Numerical examples are provided to illustrate the feasibility and accuracy of the proposed method.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Hardware & Architecture
Chen Cheng, Jun Yang, Lei Li
Summary: This article proposes an efficient reliability evaluation method for multi-state phased mission systems considering epistemic uncertainty, transmission loss, and performance storage. The method can accurately evaluate system availability and has been demonstrated to be effective through case studies.
IEEE TRANSACTIONS ON RELIABILITY
(2022)
Article
Engineering, Multidisciplinary
Xu Liu, Wen Yao, Xiaohu Zheng, Yingchun Xu, Xiaoqian Chen
Summary: Reliability analysis is crucial in the complex multi-state system (MSS) for equipment design, manufacturing, operation, and maintenance. The Universal Generating Function (UGF) method efficiently obtains system reliability, but is limited when structural relationships between subsystems or components are unclear. The Bayesian Network (BN) method is advantageous for reliability inference without explicit expressions, but less efficient for large numbers of components. To overcome these shortcomings, a novel method called UGF-BN is proposed for reliable analysis in complex MSS, improving computational efficiency and demonstrating its applicability in aircraft design optimization.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY
(2023)
Article
Engineering, Multidisciplinary
Yisha Xiang, David W. Coit, Zhicheng Zhu
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2016)
Review
Engineering, Industrial
Suzan Alaswad, Yisha Xiang
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2017)
Article
Engineering, Industrial
Suiyao Chen, Lu Lu, Yisha Xiang, Qing Lu, Mingyang Li
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2018)
Article
Operations Research & Management Science
Yisha Xiang, Jun Zhuang
ANNALS OF OPERATIONS RESEARCH
(2016)
Article
Engineering, Industrial
Yue Shi, Yisha Xiang, Mingyang Li
Article
Computer Science, Hardware & Architecture
Zhicheng Zhu, Yisha Xiang, Mingyang Li, Weihang Zhu, Kellie Schneider
IEEE TRANSACTIONS ON RELIABILITY
(2019)
Article
Engineering, Multidisciplinary
Liudong Xing, Guilin Zhao, Yujie Wang, Yisha Xiang
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2020)
Article
Engineering, Industrial
Zhicheng Zhu, Yisha Xiang
Summary: This article investigates the optimization problem of Condition-Based Maintenance for multi-component systems. It develops a multi-stage stochastic integer model and designs two efficient algorithms to solve the problem. Algorithm 1 provides optimal solutions, while Algorithm 2 heuristically finds high-quality solutions based on Algorithm 1.
Article
Operations Research & Management Science
Ying Liao, Yisha Xiang, Min Wang
Summary: This study introduces a new prognostics framework based on a higher-order hidden semi-Markov model, evaluates its performance through simulation studies, and demonstrates its practical utility through a case study on NASA turbofan engines. Additionally, it compares the model with a benchmark method, showing good predictive performance in complex systems.
NAVAL RESEARCH LOGISTICS
(2021)
Article
Engineering, Industrial
Yue Shi, Yisha Xiang, Ying Liao, Zhicheng Zhu, Yili Hong
Article
Engineering, Multidisciplinary
Liudong Xing, Guilin Zhao, Yisha Xiang, Qisi Liu
Summary: This paper proposes a new behavior-driven reliability modeling methodology for complex WSN-based smart systems, addressing challenges in analyzing system reliability efficiently. The method demonstrated its advantages in handling complex dependencies in smart systems through a case study of a smart home system.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2021)
Article
Computer Science, Interdisciplinary Applications
Zhicheng Zhu, Yisha Xiang, Bo Zeng
Summary: Maintenance optimization for complex systems consisting of multiple components has been a challenging and open issue in the literature. In this paper, a novel two-stage model and a progressive-hedging-based heuristic algorithm were developed to address this challenge. Numerical results show that the heuristic algorithm can lead to significant cost savings compared to benchmark approaches such as dynamic programming and structural policy.
INFORMS JOURNAL ON COMPUTING
(2021)
Proceedings Paper
Automation & Control Systems
Ying Liao, Yisha Xiang, Dongping Du
2020 IEEE 16TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE)
(2020)
Article
Computer Science, Interdisciplinary Applications
Yisha Xiang, Zhicheng Zhu, David W. Coit, Qianmei Feng
COMPUTERS & INDUSTRIAL ENGINEERING
(2017)
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
