Article
Engineering, Mechanical
Jiayi Ouyang, Yong Liu
Summary: This study proposes an effective approach that combines Kriging-based conditional random field with the BUS algorithm to integrate multi-type observations, in order to update the probability distribution of soil parameters and assess the slope reliability.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Civil
Yushan Liu, Luyi Li, Sihan Zhao
Summary: A novel Bayesian updating method named BUS-AK2 is proposed in this paper, which efficiently solves two key problems using two-step adaptive Kriging (AK), giving the model significant computational advantages.
Article
Engineering, Civil
Yu Xin, Zuo-Cai Wang, Jun Li, Zi-Qing Yuan, Chao Li, Wei-Chao Hou
Summary: A Bayesian based nonlinear model updating approach is proposed to enhance the post-earthquake reliability assessment of posttensioned segmental column structures. Dynamic global sensitivity analysis (DGSA) is performed to select appropriate parameters for model updating. Bayesian estimation is used to construct likelihood function based on the residual errors between measured and simulated dynamic responses. The proposed approach is validated through numerical simulations and experimental results.
ENGINEERING STRUCTURES
(2023)
Article
Engineering, Marine
Mengmeng Wang, Jiaxuan Leng, Shizhe Feng, Zhixiong Li, Atilla Incecik
Summary: This paper proposes a Markov Chain Monte Carlo (MCMC)-based Bayesian updating method to build a high-fidelity, highly-precise virtual model for offshore platforms. The method utilizes the natural frequencies and mode shapes of the platform to construct the likelihood function and generate the most probable values of the uncertain parameters through Markov Chains for updating the finite element model.
Article
Engineering, Manufacturing
Dazhou Lei, Hao Hu, Dongyang Geng, Jianshen Zhang, Yongzhi Qi, Sheng Liu, Zuo-Jun Max Shen
Summary: This study develops a flexible PLC curve forecasting framework based on Bayesian functional regression, which effectively predicts and updates sales curves, and provides insights into the interaction between promotional activities and sales curves.
PRODUCTION AND OPERATIONS MANAGEMENT
(2023)
Article
Polymer Science
Viktoria Mannheim
Summary: This paper assesses the environmental impacts of polypropylene and PP-PE-PET mixed-plastic products in the production stage using a life cycle assessment method, comparing the effects of different production methods on resource use and emissions. The research results can be used to develop more environmentally friendly injection-molding processes.
Article
Engineering, Industrial
Zeyu Wang, Abdollah Shafieezadeh
Summary: This paper presents a new approach to overcome the computational cost problem of Bayesian updating for complex computational models. It decomposes the updating problem into a set of sub-reliability problems with uncertain failure thresholds, enabling precise identification of intermediate failure thresholds and training of surrogate models. The proposed method reduces computational costs significantly while maintaining high accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Mechanical
Pengfei Ding, Xianzhen Huang, Xuewei Zhang, Changli Wang, Tianhong Gao, Miaoxin Chang, Yuxiong Li
Summary: This study proposes an improved cutting force model that considers the uncertainty of parameters and the inaccuracy of measurement. By utilizing structural reliability analysis and adaptive-kriging method, the study solves the actual distribution of parameters with uncertainty. The results provide a helpful guidance for practical machining by evaluating the influence of various parameters on the micro-milling system and exploring its reasonable selection range.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Mukesh K. Ramancha, Rodrigo Astroza, Ramin Madarshahian, Joel P. Conte
Summary: This paper introduces Bayesian model updating and identifiability analysis of nonlinear FE models for civil structures, using Pine Flat concrete gravity dam as an example. Both recursive mode with unscented Kalman filter (UKF) and batch mode with transitional Markov chain Monte Carlo (TMCMC) method are used for model updating. Identifiability and sensitivity analyses are performed using local and global methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Masaru Kitahara, Chao Dang, Michael Beer
Summary: This paper proposes a Bayesian updating approach called parallel Bayesian optimization and quadrature (PBOQ). It applies Gaussian process priors and explores a constant c in BUS through parallel infill sampling strategy. The proposed approach effectively reduces computational burden of model updating by leveraging prior knowledge and parallel computing. Numerical examples are used to demonstrate its potential benefits and advocate a coherent Bayesian fashion for BUS analysis.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Civil
Oindrila Kanjilal, Iason Papaioannou, Daniel Straub
Summary: Bayesian analysis allows for the continuous updating of failure probability in engineering systems with new data. This study introduces an adaptive importance sampling (IS) method based on cross entropy (CE) minimization, which efficiently samples the important region of the failure domain, especially for rare failure events. The method involves constructing a sample-based approximation of the posterior using CE to determine parameters that minimize Kullback-Leibler divergence from the posterior PDF. The proposed method demonstrates accuracy in estimating the reliability of engineering systems with rare failure events through numerical studies.
Article
Green & Sustainable Science & Technology
Francisco Portillo, Rosa Maria Garcia, Alfredo Alcayde, Jose Antonio Gazquez, Manuel Fernandez-Ros, Nuria Novas
Summary: This study explores the importance of sustainability and life cycle assessment in project management for considering environmental impacts. Comparing the environmental effects of supplying energy to sensor networks through public grid and off-grid systems, it is found that the off-grid option is more beneficial environmentally but less economically viable. Additionally, in-depth analysis of the battery impact in off-grid scenarios shows that reducing energy consumption can lead to improved economic benefits.
Article
Engineering, Mechanical
Antonios Kamariotis, Eleni Chatzi, Daniel Straub
Summary: The paper introduces a method of quantifying the value of information extracted from a structural health monitoring system based on the Bayesian decision analysis framework. By modeling in detail the entire process from data generation to processing, model updating, and reliability calculation, the framework is shown to provide quantitative measures on the optimality of an SHM system in a specific decision context.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Pinghe Ni, Qiang Li, Qiang Han, Kun Xu, Xiuli Du
Summary: This study proposes a Bayesian probabilistic model updating approach for substructure identification, which evaluates the uncertainties in identified results by analyzing the responses of large-scale structures. Numerical experiments on a three-span beam structure and experimental studies on an eight-floor steel frame were conducted to verify the accuracy and efficiency of the proposed method, and the results demonstrate its effectiveness.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Green & Sustainable Science & Technology
Anna Monticelli, Mattia Costamagna
Summary: The study analyzed the environmental impacts of the rental and online purchase models in the apparel industry, and found that extending the lifespan of clothing can reduce environmental impacts.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Ahmed Maged, Min Xie
Summary: In industrial settings, it is important to detect abnormal patterns in order to prevent manufacturing faults and reduce failure costs. This study proposes a new method called VIS-CNN, which uses a convolutional neural network to extract abnormal patterns from a dataset. The method can handle different input sizes and achieves a high recognition rate. The study also addresses the issue of mixed patterns and provides an improved scheme for their recognition. Overall, the method shows potential for industrial applications.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Public, Environmental & Occupational Health
Zhaoyuan Yin, Chao Fang, Haoxiang Yang, Yiping Fang, Min Xie
Summary: This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to improve the resilience of power systems. The model takes into account the uncertain impact of natural hazards and provides an optimal plan for installing and dispatching distributed energy resources. The effectiveness of the proposed approach is demonstrated through testing in the Hong Kong region.
Article
Automation & Control Systems
Yuantao Yao, Minghan Yang, Jianye Wang, Min Xie
Summary: This article proposes an end-to-end, deep hybrid network-based, short-term, multivariate time-series prediction framework for industrial processes. The framework extracts nonlinear variate correlation features using the maximal information coefficient, eliminates data uncertainty with a convolutional neural network, achieves step-ahead prediction using a bidirectional gated recurrent unit network, and optimizes the model's learning rate with an optimized Bayesian optimization method. The comparison with other state-of-the-art methods demonstrates the superiority of the proposed framework in noisy IIoT environments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Manufacturing
Feng Zhu, Xiaodong Jia, Wenzhe Li, Min Xie, Lishuai Li, Jay Lee
Summary: Unit-to-unit variation is a challenge for Fault Detection and Classification (FDC) in the semiconductor industry. Existing methods lack an evaluation of data transferability among chambers. This research proposes a methodology for data transferability evaluation and sensor screening, utilizing Time Series Alignment Kernel (TSAK) and Multidomain Discriminant Analysis (MDA) algorithm for domain generalization and feature extraction, Fisher's criterion ratios are computed for quantifying knowledge transferability, and Recursive Feature Elimination (RFE) is used for sensor selection.
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
(2023)
Article
Engineering, Industrial
Yuqing Zhang, Min Xie, Yihai He, Xiao Han
Summary: This paper proposes a capability-based approach for predicting the remaining useful life (RUL) of machining tools, which aims to evaluate the state and RUL of tools to ensure product quality. By developing physics-based and data-driven models to assess the quality capability of tools, the effectiveness and accuracy of the proposed approach are verified.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Automation & Control Systems
Zhiying Wu, Zhe Wang, Yan Wang, Junlin Xiong, Min Xie
Summary: This paper investigates the dynamic event-triggered predictive control problem for discrete-time networked control systems under deception attacks. A new dynamic event-triggered scheme is proposed, and the Luenberger observer and networked predictive control method are used. Sufficient conditions are established to guarantee the stability of the closed-loop systems. The effectiveness of the approach is validated through experiments.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Chemistry, Physical
Yiyue Jiang, Yuan Chen, Fangfang Yang, Weiwen Peng
Summary: This paper proposes an automatic feature extraction method combining convolutional autoencoder and self-attention mechanism for battery state of health (SOH) estimation. The method replaces manual feature engineering with automatic feature extraction using a convolutional autoencoder and achieves high accuracy on both the dataset used in the study and the NASA public dataset. Experimental results and comparisons with existing methods are provided.
JOURNAL OF POWER SOURCES
(2023)
Article
Engineering, Industrial
Hongyan Dui, Tianzi Tian, Shaomin Wu, Min Xie
Summary: All components and systems are prone to failure. While repairing a failed component, preventive maintenance can be conducted on other components to enhance system reliability. Choosing different components for preventive maintenance may result in different maintenance policies with varying costs. To minimize costs, engineers need appropriate tools such as importance measures to guide their selection of components for preventive maintenance. However, research that simultaneously minimizes maintenance costs and maximizes the number of components for preventive maintenance is limited. To address this gap, this paper proposes the Cost-Informed Component Maintenance Index (CICMI) as an importance index and derives propositions for this index and different maintenance policies. A method is also proposed to optimize the number of components for preventive maintenance within cost constraints. A case study on a reactor coolant system is conducted to demonstrate the applicability of the proposed methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Xuerong Ye, Qisen Sun, Ruishi Lin, Cen Chen, Min Xie, Guofu Zhai, Rui Kang
Summary: An improved reliability prediction method for DC-link electrolytic capacitors is proposed in this article to address the inadequate consideration of the self-acceleration effect in existing methods. The degradation under dynamic stress is obtained through cumulative computations and converted into degradation rate models, overcoming computational challenges and improving accuracy. The practicality and accuracy of the proposed method are demonstrated through a case study.
QUALITY ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Weiwen Peng, Zongyi Wei, Cheng-Geng Huang, Guodong Feng, Jun Li
Summary: This study proposes a novel hybrid method for high-accuracy health prognostics of PEMFCs by leveraging internal recovery effects and external health data. The health degradation of PEMFCs is characterized through voltage prediction, trend prediction, and fluctuation prediction. Experimental results demonstrate that the proposed method achieves accurate long-term predictions with small errors.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Computer Science, Hardware & Architecture
Lechang Yang, Chenxing Wang, Chunyan Ling, Min Xie
Summary: This article proposes a survival signature-based reliability framework for an imprecise multistate system (IMSS) to address the challenges of reliability evaluation for complex systems with imprecise parameters. The framework defines the survival signature and calculates the multistate survival functions based on the combination of states of composing elements. A simulation method is developed for probability estimation when imprecision is involved. An approximate Bayesian computation method with a Jensen-Shannon divergence-based kernel is developed to perform stochastic model updating and calibrate imprecise parameters. The proposed framework is validated with a numerical case of a typical bridge system and a real application example.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Automation & Control Systems
Xingchen Liu, Zhiyong Hu, Xin Wang, Min Xie
Summary: This paper studies the capacity degradation of lithium-ion batteries and analyzes the effects of cycling aging and calendar aging on battery degradation. The concept of cumulative uninterrupted cycling duration (CUCD) is introduced to capture the coupling effect between these two aging sources, and the drift rate of cycling aging is modeled using a monotonic spline. The effectiveness and superiority of the model are validated through numerical and real case studies.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Xingchen Liu, Xin Wang, Min Xie, Zhisheng Ye
Summary: Degradation analysis is crucial for system health management and remaining useful life prediction. This study proposes a framework for degradation state estimation based on distributionally robust optimization, which addresses the challenges of parameter uncertainty and measurement outlier, leading to more accurate evaluation of health status.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Min Wang, Min Xie, Yanwen Wang, Maoyin Chen
Summary: In this article, a deep quality monitoring network (DQMNet) is developed for the detection of quality-related incipient faults. DQMNet uses feature extraction and Bayesian inference to extract hidden information and construct statistics, demonstrating its superiority through numerical simulation and benchmark data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yi Ding, He Li, Feng Zhu, Zhe Wang, Weiwen Peng, Min Xie
Summary: This article proposes a novel semi-supervised method for constructing failure knowledge graphs based on maintenance logs. The method extracts hidden contextual information from maintenance records and constructs failure items and their relationships to provide decision support. The feasibility and superiority of the method are validated using real wind farm data.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
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)
