Reliability Engineering & System Safety最新文献

Data-driven global sensitivity analysis for group of random variables through knowledge-enhanced machine learning with normalizing flows

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-13 DOI: 10.1016/j.ress.2025.111007

Ziluo Xiong, Gaofeng Jia

Different approaches have been developed for evaluating Sobol’ indices for global sensitivity analysis (GSA). Among them sample-based approaches are extremely attractive because they can be purely driven by data and estimate various Sobol’ indices (e.g., first-order, higher-order, total-effects) for any individual or group of random variables using only one set of samples. However, such approaches usually rely on an accurate density estimation for the interested groups of random variables, which can be challenging for high-dimensional groups. For example, the commonly used kernel density estimation (KDE) suffers from curse of dimensionality. In this regard, this paper proposes a novel knowledge-enhanced machine learning approach for data-driven GSA for groups of random variables using sample-based approach and an emerging generative machine learning model, i.e., normalizing flows (NFs), for high-dimensional density estimation. To facilitate reliable and robust NFs training, a knowledge distillation-based two-stage training strategy is developed. Two customized loss functions are introduced, which are inspired by domain knowledge in the context of sample-based approach for GSA. Two examples are considered to illustrate and verify the efficacy of the proposed approach. Results show that introducing NFs can significantly alleviate the curse of dimensionality in the traditional sample-based approach for GSA and improve accuracy of density estimation and estimation of Sobol’ indices.

{"title":"Data-driven global sensitivity analysis for group of random variables through knowledge-enhanced machine learning with normalizing flows","authors":"Ziluo Xiong, Gaofeng Jia","doi":"10.1016/j.ress.2025.111007","DOIUrl":"10.1016/j.ress.2025.111007","url":null,"abstract":"<div><div>Different approaches have been developed for evaluating Sobol’ indices for global sensitivity analysis (GSA). Among them sample-based approaches are extremely attractive because they can be purely driven by data and estimate various Sobol’ indices (e.g., first-order, higher-order, total-effects) for any individual or group of random variables using only one set of samples. However, such approaches usually rely on an accurate density estimation for the interested groups of random variables, which can be challenging for high-dimensional groups. For example, the commonly used kernel density estimation (KDE) suffers from curse of dimensionality. In this regard, this paper proposes a novel knowledge-enhanced machine learning approach for data-driven GSA for groups of random variables using sample-based approach and an emerging generative machine learning model, i.e., normalizing flows (NFs), for high-dimensional density estimation. To facilitate reliable and robust NFs training, a knowledge distillation-based two-stage training strategy is developed. Two customized loss functions are introduced, which are inspired by domain knowledge in the context of sample-based approach for GSA. Two examples are considered to illustrate and verify the efficacy of the proposed approach. Results show that introducing NFs can significantly alleviate the curse of dimensionality in the traditional sample-based approach for GSA and improve accuracy of density estimation and estimation of Sobol’ indices.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111007"},"PeriodicalIF":9.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637291","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

Reconstruction-based Deep Unsupervised Adaptive Threshold Support Vector Data Description for wind turbine anomaly detection

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-13 DOI: 10.1016/j.ress.2025.110995

Dandan Peng , Wim Desmet , Konstantinos Gryllias

The global deployment of wind turbines as a sustainable and clean energy source underscores the criticality of early anomaly detection to ensure their safe operation, improve power generation efficiency, and reduce downtime costs. Yet, acquiring sufficient labeled and faulty data is time-consuming and expensive in practical applications, limiting the use of supervised learning methods. To this end, this paper introduces a new approach, namely the Reconstruction-based Deep Unsupervised Adaptive Threshold Support Vector Data Description (DUA-SVDD) model, for wind turbine anomaly detection. DUA-SVDD integrates reconstruction-based and boundary-based anomaly detection paradigms, synthesizing comprehensive and detailed representation information from dynamic monitoring data, encoding the distribution and patterns of normal samples across multiple levels. This model employs a joint optimization mechanism to minimize reconstruction errors and hypersphere volume simultaneously in the latent space, resolving the hypersphere collapse issue observed in Deep Support Vector Data Description (DeepSVDD). It constructs a well-structured latent space proficient in handling data noise and variations, allowing SVDD to establish more robust spherical boundaries. Additionally, it proposes an adaptive threshold algorithm based on pseudo-data to accurately differentiate abnormal from normal patterns. The method is tested and evaluated on real wind farm SCADA datasets. A comparative analysis against state-of-the-art methods highlights the superior performance of the proposed model in detecting blade icing on wind turbines, achieving average AUC values of 97.54% and 99.45% across two specific cases.

{"title":"Reconstruction-based Deep Unsupervised Adaptive Threshold Support Vector Data Description for wind turbine anomaly detection","authors":"Dandan Peng , Wim Desmet , Konstantinos Gryllias","doi":"10.1016/j.ress.2025.110995","DOIUrl":"10.1016/j.ress.2025.110995","url":null,"abstract":"<div><div>The global deployment of wind turbines as a sustainable and clean energy source underscores the criticality of early anomaly detection to ensure their safe operation, improve power generation efficiency, and reduce downtime costs. Yet, acquiring sufficient labeled and faulty data is time-consuming and expensive in practical applications, limiting the use of supervised learning methods. To this end, this paper introduces a new approach, namely the Reconstruction-based Deep Unsupervised Adaptive Threshold Support Vector Data Description (DUA-SVDD) model, for wind turbine anomaly detection. DUA-SVDD integrates reconstruction-based and boundary-based anomaly detection paradigms, synthesizing comprehensive and detailed representation information from dynamic monitoring data, encoding the distribution and patterns of normal samples across multiple levels. This model employs a joint optimization mechanism to minimize reconstruction errors and hypersphere volume simultaneously in the latent space, resolving the hypersphere collapse issue observed in Deep Support Vector Data Description (DeepSVDD). It constructs a well-structured latent space proficient in handling data noise and variations, allowing SVDD to establish more robust spherical boundaries. Additionally, it proposes an adaptive threshold algorithm based on pseudo-data to accurately differentiate abnormal from normal patterns. The method is tested and evaluated on real wind farm SCADA datasets. A comparative analysis against state-of-the-art methods highlights the superior performance of the proposed model in detecting blade icing on wind turbines, achieving average AUC values of 97.54% and 99.45% across two specific cases.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110995"},"PeriodicalIF":9.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637290","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

Remaining useful life prediction for solid-state lithium batteries based on spatial–temporal relations and neuronal ODE-assisted KAN

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-12 DOI: 10.1016/j.ress.2025.111003

Zhenxi Wang , Yan Ma , Jinwu Gao , Hong Chen

Remaining useful life prediction (RUL) of solid-state lithium batteries (SSLIBs) can accelerate the maintenance and optimization process, facing challenges in insufficient exploration of implicit degradation information, complexity of computational costs and poor interpretability. To address these issues, a novel method for obtaining comprehensive implicit information during the degradation process is proposed. Firstly, topological relations are introduced by using graph attention network (GAT) to comprehensively represent the implicit relations among external parameters. It is utilized to supplement the interdependencies between physical measurements of multiple health indicators for SSLIBs, avoiding manual feature engineering. Then, a neural ordinary differential equation (ODE) composed of Kolmogorov–Arnold network (KAN) is developed to capture the continuous dynamic implicit state trajectories during the degradation process, overcoming the issue of ignoring dynamic variations for implicit relations in external parameters. Moreover, KAN is adopt as a regressor, which ensures the interpretability of the constructed RUL prediction model for SSLIBs while reducing the computational cost. The comparison analysis in the real SSLIBs degradation datasets demonstrate the optimal minimum root mean square errors and the parameters of the model are reduced by 39.03% and 49.13%, respectively. It also indicates that the proposed method can provide new perspectives and solutions for RUL prediction of SSLIBs.

{"title":"Remaining useful life prediction for solid-state lithium batteries based on spatial–temporal relations and neuronal ODE-assisted KAN","authors":"Zhenxi Wang , Yan Ma , Jinwu Gao , Hong Chen","doi":"10.1016/j.ress.2025.111003","DOIUrl":"10.1016/j.ress.2025.111003","url":null,"abstract":"<div><div>Remaining useful life prediction (RUL) of solid-state lithium batteries (SSLIBs) can accelerate the maintenance and optimization process, facing challenges in insufficient exploration of implicit degradation information, complexity of computational costs and poor interpretability. To address these issues, a novel method for obtaining comprehensive implicit information during the degradation process is proposed. Firstly, topological relations are introduced by using graph attention network (GAT) to comprehensively represent the implicit relations among external parameters. It is utilized to supplement the interdependencies between physical measurements of multiple health indicators for SSLIBs, avoiding manual feature engineering. Then, a neural ordinary differential equation (ODE) composed of Kolmogorov–Arnold network (KAN) is developed to capture the continuous dynamic implicit state trajectories during the degradation process, overcoming the issue of ignoring dynamic variations for implicit relations in external parameters. Moreover, KAN is adopt as a regressor, which ensures the interpretability of the constructed RUL prediction model for SSLIBs while reducing the computational cost. The comparison analysis in the real SSLIBs degradation datasets demonstrate the optimal minimum root mean square errors and the parameters of the model are reduced by 39.03% and 49.13%, respectively. It also indicates that the proposed method can provide new perspectives and solutions for RUL prediction of SSLIBs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111003"},"PeriodicalIF":9.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632103","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 analysis and optimization of multi-state tree-structured systems with performance sharing mechanism

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-11 DOI: 10.1016/j.ress.2025.110990

Liudong Gu , Guanjun Wang , Yifan Zhou

Existing studies on reliability modeling of performance sharing systems (PSSs) have primarily focused on common bus or series structure. However, in some practical PSSs, units are organized in a tree structure. This paper addresses the research gap in reliability optimization of tree-structured PSSs. In such systems, units with random performance and demand are arranged in different layers. The surplus performance of each unit can be shared by the connected units located in adjacent layers. The system fails if there exists performance deficiency. A recursive method is proposed to determine the state of connected units. Additionally, a reliability evaluation algorithm is developed based on universal generating function. We further investigate the optimization of transmission capacity allocation to maximize system reliability. To streamline the search for the optimal solution, a strategy space reduction approach is introduced to derive the more appropriate value range of each decision variables, thereby simplifying the optimization process. Genetic algorithm (GA) is employed to identify the optimal solution within the optimized strategy space. Validation through two power systems demonstrates that the proposed reliability evaluation method accurately evaluates the system reliability, and the improved GA efficiently finds a superior transmission capacity allocation strategy compared to the conventional GA.

{"title":"Reliability analysis and optimization of multi-state tree-structured systems with performance sharing mechanism","authors":"Liudong Gu , Guanjun Wang , Yifan Zhou","doi":"10.1016/j.ress.2025.110990","DOIUrl":"10.1016/j.ress.2025.110990","url":null,"abstract":"<div><div>Existing studies on reliability modeling of performance sharing systems (PSSs) have primarily focused on common bus or series structure. However, in some practical PSSs, units are organized in a tree structure. This paper addresses the research gap in reliability optimization of tree-structured PSSs. In such systems, units with random performance and demand are arranged in different layers. The surplus performance of each unit can be shared by the connected units located in adjacent layers. The system fails if there exists performance deficiency. A recursive method is proposed to determine the state of connected units. Additionally, a reliability evaluation algorithm is developed based on universal generating function. We further investigate the optimization of transmission capacity allocation to maximize system reliability. To streamline the search for the optimal solution, a strategy space reduction approach is introduced to derive the more appropriate value range of each decision variables, thereby simplifying the optimization process. Genetic algorithm (GA) is employed to identify the optimal solution within the optimized strategy space. Validation through two power systems demonstrates that the proposed reliability evaluation method accurately evaluates the system reliability, and the improved GA efficiently finds a superior transmission capacity allocation strategy compared to the conventional GA.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110990"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619491","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

Integrating causal representations with domain adaptation for fault diagnosis

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-11 DOI: 10.1016/j.ress.2025.110999

Ming Jiang , Kuang Zhou , Jiahui Gao , Fode Zhang

In practical fault diagnosis, obtaining sufficient samples is often challenging. Transfer learning can help by using data from related domains, but significant distribution differences often exist due to different working conditions. To address this issue, cross-domain fault diagnosis (CDFD) has attracted increasing attention. However, most CDFD methods rely on statistical dependencies, which restricts their ability to uncover intrinsic mechanisms and affects both performance and reliability. In this paper, a Cross-domain Fault Diagnosis model based on Causal Representation learning (CFDCR) is proposed. This method employs causal representation learning with a graph autoencoder to learn invariant representations across domains, thereby improving the robustness of the prediction model. It further employs domain adversarial networks to align feature distributions, thus mitigating conditional distribution disparities between source domain data and target fault data, ultimately enhancing model performance. Experimental results on various bearing fault datasets demonstrate that the proposed cross-domain fault diagnosis model can effectively utilize related source domain data to guide fault classification tasks in the target domain and achieve more robust fault predictions.

{"title":"Integrating causal representations with domain adaptation for fault diagnosis","authors":"Ming Jiang , Kuang Zhou , Jiahui Gao , Fode Zhang","doi":"10.1016/j.ress.2025.110999","DOIUrl":"10.1016/j.ress.2025.110999","url":null,"abstract":"<div><div>In practical fault diagnosis, obtaining sufficient samples is often challenging. Transfer learning can help by using data from related domains, but significant distribution differences often exist due to different working conditions. To address this issue, cross-domain fault diagnosis (CDFD) has attracted increasing attention. However, most CDFD methods rely on statistical dependencies, which restricts their ability to uncover intrinsic mechanisms and affects both performance and reliability. In this paper, a Cross-domain Fault Diagnosis model based on Causal Representation learning (CFDCR) is proposed. This method employs causal representation learning with a graph autoencoder to learn invariant representations across domains, thereby improving the robustness of the prediction model. It further employs domain adversarial networks to align feature distributions, thus mitigating conditional distribution disparities between source domain data and target fault data, ultimately enhancing model performance. Experimental results on various bearing fault datasets demonstrate that the proposed cross-domain fault diagnosis model can effectively utilize related source domain data to guide fault classification tasks in the target domain and achieve more robust fault predictions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110999"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619492","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

Multiple classifiers inconsistency-based deep adversarial domain generalization method for cross-condition fault diagnosis in rotating systems

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-11 DOI: 10.1016/j.ress.2025.111017

Lei Gao , Qinhe Gao , Zhihao Liu , Hongjie Cheng , Jianyong Yao , Xiaoli Zhao , Sixiang Jia

Unknown fault operating conditions and the absence of fault data pose significant challenges for real-time fault diagnosis, as the generalization capability of models is heavily reliant on transferable knowledge from a single operating condition. To overcome these limitations, a novel deep adversarial domain generalization framework based on multiple classifiers inconsistency (DADG-MCI) is designed to improve generalized ability without the need for target domain data during training. Initially, unique features of the multiple source domains are captured through the probability output inconsistency of the multiple domain-specific classifiers. Subsequently, adversarial training facilitates finer-grained global feature alignment across multiple source domains, which ensures that the extracted deep features possess strong generalization capabilities. Most importantly, DADG-MCI introduces the multiple classifiers inconsistency to measure multi-domain distributional discrepancy based on Wasserstein distance, which captures feature distribution differences between domains through joint optimization of the multi-classifier module. Finally, two challenging rotating machinery fault datasets are used to evaluate the performance of DADG-MCI for cross-condition fault diagnosis. Compared to several state-of-the-art methods, DADG-MCI achieves the highest average diagnostic accuracies and successfully applies to unseen operating conditions.

未知的故障运行条件和故障数据的缺失给实时故障诊断带来了巨大挑战，因为模型的泛化能力严重依赖于单一运行条件下的可转移知识。为了克服这些限制，我们设计了一种基于多分类器不一致性的新型深度对抗域泛化框架（DADG-MCI），以提高泛化能力，而无需在训练过程中使用目标域数据。首先，通过多个特定领域分类器的概率输出不一致性来捕捉多个源领域的独特特征。随后，对抗训练有助于在多个源域之间进行更精细的全局特征对齐，从而确保提取的深度特征具有强大的泛化能力。最重要的是，DADG-MCI 引入了多分类器不一致性，以瓦瑟斯坦距离（Wasserstein distance）为基础衡量多域分布差异，通过多分类器模块的联合优化捕捉域间的特征分布差异。最后，利用两个具有挑战性的旋转机械故障数据集来评估 DADG-MCI 在跨条件故障诊断方面的性能。与几种最先进的方法相比，DADG-MCI 实现了最高的平均诊断准确率，并成功应用于未见的运行条件。

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引用次数: 0

Suggestion of specific performance shaping factor update for the human reliability analysis framework of the TRIGA research reactor

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-08 DOI: 10.1016/j.ress.2025.111010

Wasin Vechgama , Jinkyun Park , Yochan Kim , Saensuk Wetchagarun , Anantachai Pechrak , Weerawat Pornroongruengchok , Kampanart Silva

Based on the human reliability analysis (HRA) framework of the TRIGA research reactor, performance shaping factor (PSF) estimation is an important step when considering the effects of specific operating or working cultures in determining human error probabilities (HEPs). This study aims to suggest a method to develop specific PSFs for the HRA framework of the TRIGA research reactor through a TRR-1/M1 case study. The PSF survey of the HRA framework was developed based on the EMBRACE method to consider the negative impacts of each PSF compared to the normal situation of all four cognitive activities in the errors of omission and errors of commission modes. Given the varied experiences of experts, expert elicitation was employed to categorize high-performing, low-performing, and informative experts to ensure reliable data for PSF analysis. For low-performing and informative experts, the survey results of PSFs were improved by additional surveys to support an appropriate dataset for analyzing the PSFs of the HRA framework. The impact of PSFs within the HRA framework was estimated using the updated normal distribution of the posterior PSFs based on the classical model. The success likelihood index method successfully integrated all subjective expert judgments into a cohesive representation and offered a better systematic consensus model to generalize HEPs in the form of a normal distribution based on a large group of experts.

{"title":"Suggestion of specific performance shaping factor update for the human reliability analysis framework of the TRIGA research reactor","authors":"Wasin Vechgama , Jinkyun Park , Yochan Kim , Saensuk Wetchagarun , Anantachai Pechrak , Weerawat Pornroongruengchok , Kampanart Silva","doi":"10.1016/j.ress.2025.111010","DOIUrl":"10.1016/j.ress.2025.111010","url":null,"abstract":"<div><div>Based on the human reliability analysis (HRA) framework of the TRIGA research reactor, performance shaping factor (PSF) estimation is an important step when considering the effects of specific operating or working cultures in determining human error probabilities (HEPs). This study aims to suggest a method to develop specific PSFs for the HRA framework of the TRIGA research reactor through a TRR-1/M1 case study. The PSF survey of the HRA framework was developed based on the EMBRACE method to consider the negative impacts of each PSF compared to the normal situation of all four cognitive activities in the errors of omission and errors of commission modes. Given the varied experiences of experts, expert elicitation was employed to categorize high-performing, low-performing, and informative experts to ensure reliable data for PSF analysis. For low-performing and informative experts, the survey results of PSFs were improved by additional surveys to support an appropriate dataset for analyzing the PSFs of the HRA framework. The impact of PSFs within the HRA framework was estimated using the updated normal distribution of the posterior PSFs based on the classical model. The success likelihood index method successfully integrated all subjective expert judgments into a cohesive representation and offered a better systematic consensus model to generalize HEPs in the form of a normal distribution based on a large group of experts.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111010"},"PeriodicalIF":9.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Are the processing facilities safe and secured against cyber threats?

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-08 DOI: 10.1016/j.ress.2025.111011

Rajeevan Arunthavanathan , Faisal Khan , Zaman Sajid , Md. Tanjin Amin , Kalyan Raj Kota , Shreyas Kumar

Most processing facilities, including those in the chemical, petrochemical, and mineral industries, aim to operate as cyber-physical systems to achieve higher plant efficiency, productivity, and, in some cases, safety. However, this digital transformation increases the vulnerability of process control systems to cyber-attacks, which can disrupt operations and lead to catastrophic consequences. Traditional approaches often consider cybersecurity solely as an Information Technology (IT) issue, overlooking the critical role of Operational Technology (OT) in managing cyber threats and ensuring plant resilience. This article reviews OT cybersecurity challenges and solutions, culminating in developing a robust OT-specific cybersecurity framework. The proposed framework integrates threat modeling, real-time attack detection, and real-time mitigation to protect physical plant operations while ensuring operational continuity. Unlike existing models, the proposed framework bridges the safety-security gap by combining IT-driven cybersecurity strategies with OT-specific risk management and defense mechanisms. Key features of the framework include layered defense mechanisms, adaptive response strategies, and risk-based prioritization, all of which collectively strengthen resilience against advanced cyber threats. By systematically reviewing current cybersecurity practices and proposing a comprehensive framework, this study further recommends approaches to enhance scalability and practical applicability for advancing cybersecurity in process plant operations. The findings underscore the necessity of integrating IT and OT cybersecurity strategies to ensure industrial safety, security, and uninterrupted operations.

{"title":"Are the processing facilities safe and secured against cyber threats?","authors":"Rajeevan Arunthavanathan , Faisal Khan , Zaman Sajid , Md. Tanjin Amin , Kalyan Raj Kota , Shreyas Kumar","doi":"10.1016/j.ress.2025.111011","DOIUrl":"10.1016/j.ress.2025.111011","url":null,"abstract":"<div><div>Most processing facilities, including those in the chemical, petrochemical, and mineral industries, aim to operate as cyber-physical systems to achieve higher plant efficiency, productivity, and, in some cases, safety. However, this digital transformation increases the vulnerability of process control systems to cyber-attacks, which can disrupt operations and lead to catastrophic consequences. Traditional approaches often consider cybersecurity solely as an Information Technology (IT) issue, overlooking the critical role of Operational Technology (OT) in managing cyber threats and ensuring plant resilience. This article reviews OT cybersecurity challenges and solutions, culminating in developing a robust OT-specific cybersecurity framework. The proposed framework integrates threat modeling, real-time attack detection, and real-time mitigation to protect physical plant operations while ensuring operational continuity. Unlike existing models, the proposed framework bridges the safety-security gap by combining IT-driven cybersecurity strategies with OT-specific risk management and defense mechanisms. Key features of the framework include layered defense mechanisms, adaptive response strategies, and risk-based prioritization, all of which collectively strengthen resilience against advanced cyber threats. By systematically reviewing current cybersecurity practices and proposing a comprehensive framework, this study further recommends approaches to enhance scalability and practical applicability for advancing cybersecurity in process plant operations. The findings underscore the necessity of integrating IT and OT cybersecurity strategies to ensure industrial safety, security, and uninterrupted operations.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111011"},"PeriodicalIF":9.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592802","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

Dynamic reliability evaluation considering the stochastic evolving process based on extreme characteristics of system responses

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-08 DOI: 10.1016/j.ress.2025.111005

Di Zhou , Zhen Chen , Zhaoxiang Chen , Jinrui Han , Ershun Pan

The randomness of high-frequency system responses in engineering, such as vibration, stress, and displacement, poses a significant challenge to system reliability and can potentially lead to system failure. This study proposes a novel stochastic evolving process that directly incorporates random extreme values and their occurrence times, enabling the characterization of the spatial distribution and temporal evolution of dynamic system responses. By integrating the saddle-point approximation and renewal process, the proposed approach effectively captures the statistical properties and variation patterns of extreme responses. Additionally, a convolution technique is explored to handle both known and unknown process parameters. A general reliability model is formulated with rigorous theoretical reasoning to assess dynamic system performance. The unified probabilistic framework is developed that systematically integrates dynamic response evolution and different random characteristics for reliability evaluation in stochastic environments. Specifically, an analytical approach is developed for systems with memoryless properties, while a general numerical method, based on the Laplace transform, is introduced to evaluate equipment reliability under stochastic conditions in both the complex frequency and time domains. The proposed method is validated through three engineering case studies, analyzing the impact of mean values and standard deviations on system reliability. The results demonstrate strong consistency and accuracy, aligning well with Monte Carlo simulations, thereby confirming the validity and practical applicability of the approach.

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引用次数: 0

Uncertainty quantification in predicting seismic response of high-speed railway simply-supported bridge system based on bootstrap

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2025-03-07 DOI: 10.1016/j.ress.2025.111006

Lingxu Wu , Wangbao Zhou , Tianxuan Zhong , Lizhong Jiang , Tianxing Wen , Lijun Xiong , Jiang Yi

Reliable and rapid prediction of seismic-induced response is crucial for post-earthquake repair or rescue operations. In this paper, a method for quantifying uncertainty in rapid seismic response prediction for high-speed railway simply-supported bridge system (HRSBS) was developed based on a Bi-LSTM neural network surrogate model and Bootstrap resampling to address the challenge of acquiring timely seismic responses for HRSBS and the inability to determine confidence intervals from a single prediction result. Epistemic and aleatory uncertainties were quantified in rapid prediction of seismic-induced responses for HRSBS. The applicability of Bi-LSTM model based on a single seismic time series for predicting seismic-induced responses of HRSBS was identified. The results indicated that the prediction intervals with the 95% confidence level obtained by the proposed method encompass the actual values. The misjudgment rates of component damage states are effectively reduced. The Bi-LSTM model employing a single seismic time series input is suitable for predicting the time-history curves of seismic responses of components but not suitable for predicting seismic-induced residual displacement of rail.

{"title":"Uncertainty quantification in predicting seismic response of high-speed railway simply-supported bridge system based on bootstrap","authors":"Lingxu Wu , Wangbao Zhou , Tianxuan Zhong , Lizhong Jiang , Tianxing Wen , Lijun Xiong , Jiang Yi","doi":"10.1016/j.ress.2025.111006","DOIUrl":"10.1016/j.ress.2025.111006","url":null,"abstract":"<div><div>Reliable and rapid prediction of seismic-induced response is crucial for post-earthquake repair or rescue operations. In this paper, a method for quantifying uncertainty in rapid seismic response prediction for high-speed railway simply-supported bridge system (HRSBS) was developed based on a Bi-LSTM neural network surrogate model and Bootstrap resampling to address the challenge of acquiring timely seismic responses for HRSBS and the inability to determine confidence intervals from a single prediction result. Epistemic and aleatory uncertainties were quantified in rapid prediction of seismic-induced responses for HRSBS. The applicability of Bi-LSTM model based on a single seismic time series for predicting seismic-induced responses of HRSBS was identified. The results indicated that the prediction intervals with the 95% confidence level obtained by the proposed method encompass the actual values. The misjudgment rates of component damage states are effectively reduced. The Bi-LSTM model employing a single seismic time series input is suitable for predicting the time-history curves of seismic responses of components but not suitable for predicting seismic-induced residual displacement of rail.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111006"},"PeriodicalIF":9.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592801","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