Reliability Engineering & System Safety最新文献_第9页

A reliability-based approach to identify critical components in a UHVDC converter station system against earthquakes

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-27 DOI: 10.1016/j.ress.2025.110977

Huangbin Liang

{"title":"A reliability-based approach to identify critical components in a UHVDC converter station system against earthquakes","authors":"Huangbin Liang","doi":"10.1016/j.ress.2025.110977","DOIUrl":"10.1016/j.ress.2025.110977","url":null,"abstract":"<div><div>Earthquakes pose a huge threat to the power system in seismically active regions. Ultra-High Voltage Direct Current (UHVDC) converter stations become integral to modern power grids, especially for long-distance power transmission, and thus understanding and improving their seismic reliability is essential for ensuring the robustness of the power system. This paper presents a comprehensive reliability-based approach to identify critical components within UHVDC converter stations, focusing on seismic reliability. A seismic reliability index is defined as the expected post-earthquake transmission capacity loss, considering both the earthquake probability and the derated capacity under different operation modes. The converter system's seismic reliability model is established based on divide-and-group principles, dividing it into subsystems and deriving an equivalent logical model based on their interdependency. Failure probabilities of subsystems, consisting of wire-interconnected electrical equipment, are determined through finite element models and seismic vulnerability analysis, accounting for wire interaction forces. Advanced sensitivity analysis techniques such as the Morris method and Sobol's analysis identify critical components influencing seismic reliability. A case study on a real-world ±800 kV UHVDC converter station system demonstrates the effectiveness of the proposed approach in enhancing seismic reliability efficiently.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110977"},"PeriodicalIF":9.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel model and simulation method for multivariate Gaussian fields involving nonlinear probabilistic dependencies and different variable-wise spatial variabilities

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-27 DOI: 10.1016/j.ress.2025.110963

Meng-Ze Lyu , Yang-Yi Liu , Jian-Bing Chen

{"title":"A novel model and simulation method for multivariate Gaussian fields involving nonlinear probabilistic dependencies and different variable-wise spatial variabilities","authors":"Meng-Ze Lyu , Yang-Yi Liu , Jian-Bing Chen","doi":"10.1016/j.ress.2025.110963","DOIUrl":"10.1016/j.ress.2025.110963","url":null,"abstract":"<div><div>The inherent randomness of engineering structures significantly influences the analysis of structural stochastic responses and safety assessments. It is critical to quantify the three aspects of random fields, including the randomness of individual variables, the probabilistic interdependence among multiple variables, and the spatiotemporal correlation of fields. This paper introduces a novel modeling framework for multivariate fields that accommodates both nonlinear probabilistic dependencies captured through copula, and the distinct spatial variability of individual fields described by correlation functions. Specifically, the framework defines a new analytical function, termed the bridge function, which establishes the relationship between the correlation functions of two fields governed by any copula structure. This proves the consistency of the new model, i.e., the copula function, as a between-variable constraint, allows the spatial correlation function of different variables to be freely selected, either with different correlation length or even with different shape. Further, to facilitate simulation, by the bridge function samples from multiple independent Gaussian fields can be onverted into those of multivariate fields that involve the specified vine copula dependencies and individual correlation functions. This approach addresses the challenge of simultaneously satisfying nonlinear dependencies and spatial variability in multivariate field simulations. The paper details the analytical expressions and numerical solution procedures for the bridge function, along with a comprehensive simulation method that integrates vine-copula-based conditional sampling and stochastic harmonic functions. The effectiveness of the proposed method is validated through various engineering application case studies, demonstrating its potential for accurate uncertainty quantification in complex engineering scenarios.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110963"},"PeriodicalIF":9.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning-stochastic ensemble for RUL prediction and predictive maintenance with dynamic mission abort policies

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-25 DOI: 10.1016/j.ress.2025.110919

Faizanbasha A. , U. Rizwan

{"title":"Deep learning-stochastic ensemble for RUL prediction and predictive maintenance with dynamic mission abort policies","authors":"Faizanbasha A. , U. Rizwan","doi":"10.1016/j.ress.2025.110919","DOIUrl":"10.1016/j.ress.2025.110919","url":null,"abstract":"<div><div>Accurate prediction of Remaining Useful Life (RUL) is crucial for optimizing maintenance strategies in industrial systems. However, existing models often falter under nonlinear and nonstationary degradation conditions with stochastic and abrupt failures, limiting their real-world effectiveness. To address this, we introduce a novel approach that combines advanced deep learning architectures with stochastic modeling and dynamic optimization techniques for more precise RUL prediction. This study has three overarching aims: First, to propose a hybrid ensemble model integrating convolutional neural networks, transformers, long short-term memory networks, and a smooth semi-martingale stochastic layer, a combination not previously explored, to effectively model both deterministic and stochastic degradation processes, thereby enhancing RUL prediction accuracy. Second, to introduce a reinforcement learning-based hyperparameter tuning method that dynamically adjusts model parameters, improving performance and reducing training time, which in turn optimizes the ensemble model’s predictive capabilities. Third, to integrate the refined RUL predictions and time-varying thresholds into a multi-stage optimization framework for mission cycle assignment and resource management. This facilitates real-time decision-making and the development of a dynamic mission abort policy, including mission shifting, re-engagement, post-abortion analysis, mission plan adjustments, and maintenance scheduling. Together, these innovations enhance RUL prediction accuracy, model adaptability, and operational efficiency, ensuring reliable and cost-effective maintenance strategies in mission-critical systems. The proposed model, validated using NASA’s C-MAPSS dataset, demonstrated superior RUL prediction accuracy over state-of-the-art methods, with sensitivity analyses and ablation studies confirming its stability and effectiveness.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110919"},"PeriodicalIF":9.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk propagation mechanisms in railway systems under extreme weather: A knowledge graph-based unsupervised causation chain approach

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-25 DOI: 10.1016/j.ress.2025.110976

Yujie Huang, Zhipeng Zhang, Hao Hu

{"title":"Risk propagation mechanisms in railway systems under extreme weather: A knowledge graph-based unsupervised causation chain approach","authors":"Yujie Huang, Zhipeng Zhang, Hao Hu","doi":"10.1016/j.ress.2025.110976","DOIUrl":"10.1016/j.ress.2025.110976","url":null,"abstract":"<div><div>Frequent and intensive adverse weathers can cause severe rail accidents through domino effect, posing significant challenges to railway safety and operational reliability. A detailed elucidation of the risk propagation mechanism across hazardous events is critical for effective risk management in rail transportation. Risk pathways involve various meteorological factors, infrastructure vulnerabilities, and consequences, in which each exhibits distinct causation strengths, trigger probabilities, severity levels, and high-impact points. To disclose the characteristics of weather-related railway domino effect accidents, this paper develops a novel railway causation analysis methodology based on an event logic graph. This framework enhances existing knowledge graph-based methodologies by emphasizing the evolution and logical progression of sequential hazardous events. Besides, an unsupervised accident causation chain linking technique is proposed, which integrates historical accident data into the knowledge graph to build a comprehensive graph database. It facilitates data-driven analysis of both structured and unstructured accident records without requiring laborious annotations. By thoroughly evaluating topological features and statistical indicators via a real-world dataset of weather-related railway accidents, key risk propagation patterns such as risk path dependence, path convergence, and risk escalation curves are recognized. Critical nodes including risk amplifiers, critical junctures, and marginal risk contributors within six critical domino chains are identified. These findings inform targeted risk mitigation strategies to prevent risk propagation and escalation. The proposed methodology and results offer theoretical support and actionable insights for enhancing safety and reliability management of railway systems under extreme weather conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110976"},"PeriodicalIF":9.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent decision on shield construction parameters based on safety evaluation model and sparrow search algorithm

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-25 DOI: 10.1016/j.ress.2025.110973

Wei Gao, Shuangshuang Ge, Shuang Cui, Xin Chen

{"title":"Intelligent decision on shield construction parameters based on safety evaluation model and sparrow search algorithm","authors":"Wei Gao, Shuangshuang Ge, Shuang Cui, Xin Chen","doi":"10.1016/j.ress.2025.110973","DOIUrl":"10.1016/j.ress.2025.110973","url":null,"abstract":"<div><div>Determination the suitable shield construction parameters is an important work to ensure the engineering construction safety. Traditional method for determination construction parameters relies on people's experience and lacks precision and adaptability. To overcome this limitation, based on the safety evaluation model using deep learning method of whale optimizing deep belief network (WO-DBN) and optimization method of sparrow search algorithm (SSA), one new method has been proposed. In this method, the WO-DBN, in which the whale optimization algorithm (WOA) has been used to select the suitable parameters of deep belief network (DBN), has been applied to safety evaluation of shield tunnel construction, and SSA has been used to determine the suitable construction parameters. By this method, the suitable construction parameters can be automatically determine by their adjustment sequence according to the sensitivity for safety, and the construction control standard is satisfied simultaneously. Then, the new method has been applied to the shield tunneling of Guangzhou subway line 18 in China to determine the suitable construction parameters for the safety evaluation objectives (ground settlement and segment floating). The results show that by the obtained construction parameters, the safety control standards for ground settlement and segment floating can be improved. This shows that the proposed method has a great contribution to improve the safety of construction by about 68 % and 89 %, respectively. In the future, the physical information and geological uncertainty can be considered in this method, which is the in-depth research to improve its adaptability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110973"},"PeriodicalIF":9.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated multi-agent-based outpatient building fire response modeling for risk-driven resource use and retrofitting strategies: A case study

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-25 DOI: 10.1016/j.ress.2025.110970

Aokun Yu , Haichao Bu , Tianyi Luan , Wenmei Gai

{"title":"Integrated multi-agent-based outpatient building fire response modeling for risk-driven resource use and retrofitting strategies: A case study","authors":"Aokun Yu , Haichao Bu , Tianyi Luan , Wenmei Gai","doi":"10.1016/j.ress.2025.110970","DOIUrl":"10.1016/j.ress.2025.110970","url":null,"abstract":"<div><div>To evaluate and optimize the evacuation capability of an outpatient building from a risk-driven perspective, a multi-agent-based modeling framework for emergency response that integrates the processes of fire evolution, emergency evacuation and rescue was developed. Then explored evacuation capacity optimization strategies from the perspectives of resource use and building retrofitting. To verify the effectiveness and applicability of the method, an evacuation model that simulates a fire in the outpatient building was developed. It was found that few occupants with restricted mobility took up the most evacuation time, need to improve the vertical mobility of these occupants; To prevent unplanned evacuation network interruptions due to fire, stairwell availability needs to be protected; The use of elevators for evacuation needs to be centralized to speed up the evacuation. The results of the current study will help public emergency authorities develop an effective plan for optimizing the emergency response system in outpatient buildings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110970"},"PeriodicalIF":9.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adaptive mixture prior in Bayesian convolutional autoencoder for early detecting anomalous degradation behaviors in lithium-ion batteries

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110926

Sun Geu Chae, Suk Joo Bae

{"title":"An adaptive mixture prior in Bayesian convolutional autoencoder for early detecting anomalous degradation behaviors in lithium-ion batteries","authors":"Sun Geu Chae, Suk Joo Bae","doi":"10.1016/j.ress.2025.110926","DOIUrl":"10.1016/j.ress.2025.110926","url":null,"abstract":"<div><div>Accurate and timely detection of anomalies in lithium-ion batteries is crucial for ensuring their reliability and safety. Complex degradation patterns and limited availability of labeled data pose significant challenges in identifying abnormal behaviors in battery usage. This paper proposes an unsupervised adaptive mixture distribution-based Bayesian convolutional autoencoder (AMDBCAE) method for detecting anomalous degradation behaviors in lithium-ion batteries at earlier cycles of reliability test. As the prior for the model parameters, we propose a mixture of the Laplace and Student’s <span><math><mi>t</mi></math></span> distributions by taking uncertainties in the weights of the convolutional network and their heavy-tailed characteristics into account. Using a modified form of the Bayes by backprop algorithm, the parameter of mixture proportion is adaptively updated to capture diverse and complex degradation patterns in battery degradation data more efficiently. Extracted latent features are then processed through unsupervised clustering algorithms to identify abnormal degradation behaviors of lithium-ion batteries. The analyses of two real-world lithium-ion battery datasets demonstrate the efficiency and accuracy of the proposed unsupervised framework with limited number of testing data. The proposed method addresses the limitations of manual feature extraction and the need for extensive experimental knowledge by leveraging the adaptive BCAE model to automatically extract latent features as a virtual health indicator in sparse data environments.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110926"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge embedded spatial–temporal graph convolutional networks for remaining useful life prediction

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110928

Xiao Cai , Dingcheng Zhang , Yang Yu , Min Xie

{"title":"Knowledge embedded spatial–temporal graph convolutional networks for remaining useful life prediction","authors":"Xiao Cai , Dingcheng Zhang , Yang Yu , Min Xie","doi":"10.1016/j.ress.2025.110928","DOIUrl":"10.1016/j.ress.2025.110928","url":null,"abstract":"<div><div>Accurate prediction of remaining useful life (RUL) is crucial for prognostics and health management of equipment. Deep learning methods have gained significant attention in this field, leveraging the abundance of monitoring data captured from sensor networks. However, these methods often overlook the spatial interactions among sensor signals. Moreover, they primarily focus on pattern extraction from sensor data and neglect the utilization of available prior knowledge that could enhance prediction accuracy and stability. To address these limitations, a knowledge-embedded spatial–temporal graph convolutional networks (KEST-GCN) method is proposed. In KEST-GCN, the relationship triplets are established based on the system structure knowledge and sensor position information. Then, these triplets are transformed into low-dimensional vector embeddings using an energy-based knowledge embedded algorithm. After that, the graph dataset is generated, where the embeddings of sensors are utilized to construct the graph edges and weighted adjacency matrix. The weights are dynamically updated by an attention mechanism. Finally, a GCN layer with a multi-head attention mechanism, an LSTM layer and a fully connected layer are employed to extract spatial–temporal degradation patterns and obtain the RUL prediction results. The effectiveness and stability of our proposed method is demonstrated using an aero-engine dataset and a cutting tool dataset, respectively.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110928"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability improvement of rolling stock planning with maintenance requirements for high-speed railway

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110972

Jiaxin Niu, Ke Qiao, Peng Zhao

{"title":"Reliability improvement of rolling stock planning with maintenance requirements for high-speed railway","authors":"Jiaxin Niu, Ke Qiao, Peng Zhao","doi":"10.1016/j.ress.2025.110972","DOIUrl":"10.1016/j.ress.2025.110972","url":null,"abstract":"<div><div>The rolling stock planning problem is a key step in the high-speed railway transit planning process. When trip delays, railway departments may need to incur considerable additional costs for plan adjustments to quickly resume operations. Therefore, we consider the impact of trip delay probabilities during rolling stock planning, aiming to minimise the total travel cost and maximise plan robustness to enhance the reliability of plan execution. A space–time–state network is established to describe the operation of rolling stocks considering accumulated mileage and running time constraints for maintenance, representing the rolling stock planning problem as a mixed-integer nonlinear programming model. Then, an alternating direction method of multipliers (ADMM)-based decomposition mechanism that decomposes the model into a set of rolling stock route selection subproblems is introduced, where each subproblem is efficiently solved by a maintenance-constrained shortest path algorithm related to reliability. A set of different scale real-life cases based on trips managed by a depot in China are used to verify the effectiveness of the proposed model and algorithm. The results show that the ADMM considerably outperforms the traditional Lagrangian relaxation (LR) method. On a set of larger-scale cases, the proposed ADMM with enhancements obtains an optimality gap of 2.42 % on average. This result is substantially better than LR, which provides optimality gaps of 32.55 % on average. Finally, the model in this paper effectively enhances the probability of successful rolling stock route execution in trip delay scenarios, resulting in a rolling stock plan with improved reliability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110972"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Creating an incident investigation framework for a complex socio-technical system: Application of multi-label text classification and Bayesian network structure learning

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110971

Mohammadreza Karimi Dehkordi, Fereshteh Sattari, Lianne Lefsrud

{"title":"Creating an incident investigation framework for a complex socio-technical system: Application of multi-label text classification and Bayesian network structure learning","authors":"Mohammadreza Karimi Dehkordi, Fereshteh Sattari, Lianne Lefsrud","doi":"10.1016/j.ress.2025.110971","DOIUrl":"10.1016/j.ress.2025.110971","url":null,"abstract":"<div><div>The power distribution sector presents a complex socio-technical system where accidents frequently occur from various technical, human, environmental, and organizational factors, resulting in fatalities and substantial economic losses. The dynamic operational environment and complex interactions among the causal factors further complicate effective risk management and accident prevention. This research proposes a methodology to identify various risk factors and develop causal networks representing the complex relationships among these factors in power distribution incident reports. First, machine learning multi-label text classification identifies the risk factors from the incident reports. Then, the relationship among these factors is determined by integrating experts’ domain knowledge and data-driven Bayesian network structure learning approaches. Finally, the most influential causal factors and their direct/indirect effects on the incidents are identified, and proper risk control measures are recommended. The proposed methodology is applied to an incident database from a Canadian power distribution company, covering power outages, injuries, environmental issues, and near misses collected from 2013 to 2020. The results highlight that human and technical factors are the most influential and affected by organizational and environmental factors. Considering their complex interaction, implementing targeted risk management for high-risk direct/indirect causal factors could prevent further incidents and improve the companies’ overall safety.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110971"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0