Article
Engineering, Industrial
Sophie Charlotte Stebner, Juri Martschin, Bahman Arian, Stefan Dietrich, Martin Feistle, Sebastian Huetter, Remi Lafarge, Robert Laue, Xinyang Li, Christopher Schulte, Daniel Spies, Ferdinand Thein, Frank Wendler, Malte Wrobel, Julian Rozo Vasquez, Michael Doelz, Sebastian Muenstermann
Summary: Recent trends in manufacturing engineering disciplines highlight the importance of monitoring techniques in sustainable and economically efficient design of forming processes. Open-loop controlled processes currently neglect the microstructure evolution that determines final product properties. Implementing closed-loop control can significantly improve efficiency by adjusting process actuators according to required product properties, thus reducing postproduction. A systematic understanding of the relationship between dimensional accuracy after forming and final product properties is crucial for successful closed-loop property controls.
ADVANCES IN INDUSTRIAL AND MANUFACTURING ENGINEERING
(2024)
Article
Engineering, Industrial
M. Rempel
Summary: This article examines a major maritime disaster scenario and explores the evacuation process in such a situation. The study finds that there are various factors that affect the number of lives saved, including the uncertainty of individuals' medical condition, the arrival time of maritime and air assets, and the decision policies used. The authors formulate the multi-domain operation as a sequential decision problem using a modeling framework and provide decision support through a hypothetical case study.
Article
Engineering, Industrial
Zhiyuan Jiang, Xianzhen Huang, Bingxiang Wang, Xin Liao, Huizhen Liu, Pengfei Ding
Summary: This paper presents a time-dependent reliability-based design optimization approach for main shaft bearings, considering the maximization of fatigue life and the elastohydrodynamic film thickness as optimization objectives. An efficient two-stage enrichment strategy is introduced to handle the time-dependent probabilistic constraint. The proposed approach is demonstrated to be effective and robust through a real application in a 5 MW wind turbine.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xiaojun Sun, Ding Feng, Qiang Zhang, Sheng Lin
Summary: This paper proposes an optimal substation siting method considering seismic risks. By integrating earthquake scenario simulation and substation fragility analysis, the method mitigates seismic risks in the traction power supply system (TPSS) planning and provides theoretical bases for disaster prevention and mitigation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tong Zhou, Tong Guo, You Dong, Fan Yang, Dan M. Frangopol
Summary: A new look-ahead learning function called stepwise margin reduction (SMR) is proposed for active learning reliability analysis based on the concept of limit-state margin probability function. SMR aims to select the best next point minimizing the integrated margin probability function, and it reduces the computational burden through closed-form expression, localized integral scheme, and pruned candidate pool. Results demonstrate that SMR outperforms traditional methods in terms of accuracy and efficiency.
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)
Article
Engineering, Industrial
Xia Jiang, Zhenzhou Lu
Summary: This paper proposes a quantile-based sequential optimization and reliability assessment (QSORA) method to overcome the shortcomings of the nested solution strategy in safety life analysis. The proposed method improves the efficiency and accuracy of safety life analysis.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Glenn Emmers, Tom Van Acker, Johan Driesen
Summary: This paper presents a methodology for modeling the availability of low-voltage direct current (LVDC) systems with battery storage. It addresses the challenges posed by component failure, the presence of many components, and the addition of battery storage to the system. The methodology combines component reliability models, semi-Markov availability models, the universal generating operator (UGO) method, and a stochastic battery reserve time analysis, offering increased accuracy while remaining tractable and easy to understand.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nick Eleftheroglou, Georgios Galanopoulos, Theodoros Loutas
Summary: This study proposes a new stochastic model, SLHSMM, to address the challenge of reliable RUL prediction in cases with unexpected phenomena. By assigning higher importance to training structures with greater similarity to the testing structure, the estimated parameters effectively capture the specific characteristics of the testing structure.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yue Shang, Maria Nogal, Rui Teixeira, A. R. (Rogier) M. Wolfert
Summary: This paper proposes a new sensitivity method focusing on extreme response and structural limit states. The method utilizes polynomial chaos expansion to approximate model output, and it is verified and illustrated on two engineering structures. The method clarifies the role of input factors in response variability under different design criteria.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Review
Engineering, Industrial
D. Scorgie, Z. Feng, D. Paes, F. Parisi, T. W. Yiu, R. Lovreglio
Summary: This study investigates the application and effectiveness of VR safety training solutions in various industries such as construction, fire, aviation, and mining. The findings suggest a need for more studies that adopt theories and measure long-term retention. Two meta-analyses demonstrate that VR safety training outperforms traditional training in terms of knowledge acquisition and retention.
Article
Engineering, Industrial
Jian Wang, Huiyuan Liu, Shibin Gao, Long Yu, Xingyang Liu, Dongkai Zhang, Lei Kou
Summary: This paper proposes a robust deep Gaussian process (DGP)-based uncertainty-aware trustworthy prediction approach to the fog-haze-caused pollution flashover (FHPF) risk of overhead contact line (OCL) insulators. The key parameters affected by fog-haze on insulator surface contamination are identified, and a sampling-based inference method is used to handle uncertainty. Experimental results demonstrate the effectiveness and superiority of the proposed method compared to other predictive classification methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hao Sun, Haiqing Wang, Ming Yang, Genserik Reniers
Summary: To maintain continuous production, chemical plant operators may choose to ignore or handle faults online rather than shutting down process systems. However, the interaction and interdependence between components in a digitalized process system are significant, and faults can propagate to downstream nodes, potentially leading to risk accumulation and major accidents. This study proposes a dynamic risk assessment method that integrates the system-theoretic accident model and process approach (STAMP) with the cascading failure propagation model (CFPM) to model the risk accumulation process. The proposed method is applied to a Chevron refinery crude unit and demonstrates its effectiveness in quantifying the process of risk accumulation and providing real-time dynamic risk profiles for decision-making.
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(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
Jinfeng Zhang, Mei Jin, Chengpeng Wan, Zhijie Dong, Xiaohong Wu
Summary: This paper introduces a novel scenario analysis framework based on disaster system theory for assessing collision risks of inland intelligent ships. The results show that the application of intelligent technologies can reduce the occurrence probability of collision accidents and mitigate the severity of their consequences. This research provides a framework for the safety evaluation of inland intelligent ships.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Haijie Wang, Bo Li, Liming Lei, Fuzhen Xuan
Summary: This study proposes a probabilistic neural network framework integrating physical information for predicting the fatigue life of additively manufactured components. The experimental results confirm that this framework can predict fatigue life more accurately and provide more reliable prediction performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wenzhe Ding, Xiang Bai, Qingwei Wang, Fang Long, Hailin Li, Zhengrong Wu, Jian Liu, Huisheng Yao, Hong Yang
Summary: This paper proposes a method for designing a truncated test scheme for success-failure in-orbit tests, which compresses the continuous test area and obtains a smaller upper limit of the IOT sample size by introducing the truncated Bayes-sequential mesh test method and incorporating optimization theory. The specific calculation formula for the BayesSMT critical line is derived, and the occurrence probabilities of each acceptance and rejection point are calculated using the Markov chain model. Finally, an optimal truncated test optimization algorithm based on the augmented lagrangian genetic algorithm is proposed.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Juan Fernandez, Juan Chiachio, Jose Barros, Manuel Chiachio, Chetan S. Kulkarni
Summary: This manuscript proposes a new physics-guided Bayesian recurrent neural network, which combines the advantages of physics-based models, recurrent neural networks, and Bayesian methods. The algorithm significantly improves the accuracy in multistep-ahead forecasting, provides stability during multiple runs, and accurately quantifies uncertainty. The algorithm has been applied to fatigue in composites and accelerations in concrete buildings, with comparable accuracy to state-of-the-art recurrent neural networks.
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)
