Article
Engineering, Industrial
Qidong You, Jianbin Guo, Shengkui Zeng, Haiyang Che
Summary: During abnormal situations, situation awareness (SA) plays a critical role in ensuring system safety. This study focuses on short-term multi-round SA (STMR-SA) and examines the impact of anchoring effect (AE) and confirmation bias (CB) on STMR-SA errors. A novel reliability assessment method is proposed, considering the round dependencies caused by AE and CB. The method is demonstrated through a case study on the Boeing 737-8 (MAX) accident, showing that improvement measures can enhance STMR-SA reliability and system safety.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yulu Zhang, Guanghan Bai
Summary: This paper studies the variable system cost methodology in life cycle and develops maintenance strategies by identifying failure modes to improve system reliability. The effectiveness of the proposed method is demonstrated through the development of selection schemes and a case study comparison.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Zhiwei Chen, Hao Zhang, Xinyue Wang, Jinling Yang, Hongyan Dui
Summary: This paper proposes a method to improve the reliability of satellite communication systems by using Markov Bayesian Networks and redundancy design strategies. It introduces a Bayesian environmental importance measure that considers the unique attributes of the space environment to verify the significance of subsystems. Furthermore, a multi-objective particle swarm optimization algorithm is employed to solve the optimal number of redundancies for critical subsystems.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Engineering, Industrial
Chenyang Ma, Xianzhi Wang, Yongbo Li, Zhiqiang Cai
Summary: Compound fault diagnosis is crucial for the reliability and security of manufacturing equipment. Existing methods mainly focus on untrained compound faults, neglecting more common single faults in the test set. To address these issues, a novel broad zero-shot diagnosis method (BZSD) is proposed, which can identify both single faults and untrained compound faults, improving diagnostic accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jian Guo, Kaijiang Ma
Summary: This study aims to analyze the risk of transporting hazardous chemicals on sea-crossing bridges using a dynamic Bayesian network (DBN) model. The study found that vehicle failure has the highest impact on the transportation system and should be given more attention. Additionally, wind sensitivity to the sea-crossing bridge transportation system is significant and cannot be ignored.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Krzysztof Woloszyk, Yordan Garbatov
Summary: This paper proposes a probabilistic-driven framework for enhanced corrosion estimation of ship structural components using Bayesian inference and limited measurement data. The framework incorporates measurement uncertainty and provides confidence intervals for the mean value and standard deviation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Md. Tanjin Amin, Giordano Emrys Scarponi, Valerio Cozzani, Faisal Khan
Summary: This article examines the effectiveness and accuracy of threshold-based and probit-based methods in assessing domino effects. The results indicate that threshold-based methods are not suitable for quantitative assessment, and there are limitations in probit-based methods for time-dependent domino effect assessment. By utilizing site-specific structural response data and data analytics, a new improved time to failure prediction model is proposed, which demonstrates better performance compared to existing models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Shital Thekdi, Terje Aven
Summary: This paper examines biases in risk studies and investigates how to identify and address them to ensure high-quality risk analysis. By considering biases related to systematic error, event inclusion, models, and cognitive factors, the paper explores their influence on risk characterization. The insights gained from this exploration can be valuable to risk analysts, policymakers, and other stakeholders involved in risk study applications.
Article
Engineering, Industrial
Maryam Lari
Summary: Occupational health and safety (OHS) are crucial for employee well-being and productivity. This study examines the impact of OHS practices on employee productivity in a UAE Fire and Security company, finding that OHS interventions can enhance workplace ambiance and significantly boost employee productivity.
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Engineering, Industrial
Xiaoliang Yan, Reed Williams, Elena Arvanitis, Shreyes Melkote
Summary: This paper extends prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, providing manufacturability feedback and labels of candidate machining operations for query 3D parts.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)
Article
Engineering, Industrial
Danko J. Jerez, M. Chwala, Hector A. Jensen, Michael Beer
Summary: This paper proposes a framework for designing optimal borehole configurations for shallow foundation systems under undrained soil conditions. It minimizes the standard deviations of the bearing capacity to ensure performance. The method adopts a random failure mechanism for evaluating random bearing capacity and provides sensitivity information of the selected performance measure.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xucong Huang, Zhaoqin Peng, Diyin Tang, Juan Chen, Enrico Zio, Zaiping Zheng
Summary: Health Indicators (HIs) are widely used for health state assessments. However, obtaining the true value of HIs with physical meaning is difficult in many cases. Therefore, this study proposes a physics-informed autoencoder that combines a physics-based model with deep learning approaches to construct HIs effectively. The proposed framework redefines the conventional HI construction process by mapping sensor readings to a degradation-represented latent space using a deep learning model, and then reconstructing the sensor readings using a physics-based model.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Arto Niemi, Bartosz Skobiej, Nikolai Kulev, Frank Sill Torres
Summary: Offshore wind farms are critical infrastructures for energy production, and their protection and resilience are gaining increased attention. This paper models failures in offshore wind farms and studies how maintenance services can sustain or recover operations under different stressors. The results show various challenges to operations, which can be used to test and define requirements for future countermeasures to improve resilience.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Eyitayo A. Opabola, Carmine Galasso
Summary: Post-disaster recovery is a significant challenge, especially in developing countries. Various technical, environmental, socioeconomic, political, and cultural factors substantially influence post-disaster recovery. This study introduces a probabilistic framework for modeling the post-disaster recovery of buildings and electric power networks (EPNs) in developing countries.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Sen Yang, Yi Zhang, Xinzheng Lu, Wei Guo, Huiquan Miao
Summary: This study proposes a novel decision support model that integrates graph theory and neural network to determine optimal restoration policies for maximizing disaster resilience. The model utilizes stochastic scheduling and deep reinforcement learning algorithm to efficiently make repair decisions, and demonstrates higher performance and computational efficiency in a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Phong T. T. Nguyen, Lance Manuel
Summary: In this study, the combination of sliced inverse regression (SIR) and polynomial chaos expansion is explored for predicting the long-term extreme response of offshore structures. By reducing the dimensionality of the problem, this method can alleviate the curse of dimensionality while improving efficiency and accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Meng Shi, Zhichao Zhang, Wenke Zhang, Yi Ma, Hanbo Li, Eric Wai Ming Lee
Summary: This study investigates pedestrian behaviours and evacuation processes in both fire and non-fire conditions using Minecraft. The results demonstrate the potential of Minecraft for realistically simulating evacuation processes, as the behaviours and flow patterns of pedestrians in virtual experiments fit well with real-life experiments. The study also shows that pedestrians exhibit fire avoidance behaviours and orderly queuing during a fire emergency, resulting in faster evacuation.
Article
Engineering, Industrial
Sara Kohtz, Junhan Zhao, Anabel Renteria, Anand Lalwani, Yanwen Xu, Xiaolong Zhang, Kiruba Sivasubramaniam Haran, Debbie Senesky, Pingfeng Wang
Summary: This paper presents a data-driven reliability-based design optimization method for sensor placement and fault detection of a permanent magnet synchronous motor (PMSM). The method utilizes a digital twin model for fault simulation and achieves optimized sensor placement and fault classification through distance clustering and genetic algorithms, demonstrating high accuracy and computational efficiency.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
