Pub Date : 2025-04-21DOI: 10.1016/j.ress.2025.111135
Luciano Sanchez , Nahuel Costa , Ines Couso
This paper introduces a method for augmenting sensor data using Physically Informed Echo State Networks (ESNs), which facilitates system identification in scenarios with limited sensor data. The approach integrates domain-specific physical knowledge into the learning process of ESNs to generate surrogate time-amplitude signals from the Power Spectral Density (PSD) of the data and a predefined list of system excitation frequencies. This integration proves particularly beneficial during the initial design phases of condition monitoring systems, where empirical data is often sparse. We demonstrate the effectiveness of this method through experiments on a 30 kW jet fan in a road tunnel ventilation system. Results indicate significant improvements in the operational capabilities of condition monitoring systems for newly developed equipment. This method is versatile and applicable across various industrial contexts with insufficient historical operational data.
{"title":"Addressing data scarcity in industrial reliability assessment with Physically Informed Echo State Networks","authors":"Luciano Sanchez , Nahuel Costa , Ines Couso","doi":"10.1016/j.ress.2025.111135","DOIUrl":"10.1016/j.ress.2025.111135","url":null,"abstract":"<div><div>This paper introduces a method for augmenting sensor data using Physically Informed Echo State Networks (ESNs), which facilitates system identification in scenarios with limited sensor data. The approach integrates domain-specific physical knowledge into the learning process of ESNs to generate surrogate time-amplitude signals from the Power Spectral Density (PSD) of the data and a predefined list of system excitation frequencies. This integration proves particularly beneficial during the initial design phases of condition monitoring systems, where empirical data is often sparse. We demonstrate the effectiveness of this method through experiments on a 30 kW jet fan in a road tunnel ventilation system. Results indicate significant improvements in the operational capabilities of condition monitoring systems for newly developed equipment. This method is versatile and applicable across various industrial contexts with insufficient historical operational data.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111135"},"PeriodicalIF":9.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863583","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}
Pub Date : 2025-04-21DOI: 10.1016/j.ress.2025.111125
Yanming Li , Xiaojuan Qin , Min Chai , Haoran Wu , Fujing Zhang , Fenghe Jiang , Changbao Wen
Due to the increasing interest in the security of the battery system, precise and rapid work on the state of health (SOH) and remaining useful life (RUL) evaluation of lithium batteries (LIBs) is necessary in practice. In this article, a MC-CNN-TimesNet model is proposed to predict the SOH and RUL of lithium batteries. This model captures the deep-state characteristics of lithium battery aging by capturing the dependencies within and between different time scales. In addition, a Tree-structured Parzen Estimation (TPE) algorithm is used in the optimization of model parameters. In this study, we also conducted correlation analysis by Pearson Correlation Coefficient (PCC) on the input voltage, current, temperature, time, and capacity data to select the features with higher correlation with SOH and RUL. Based on the Principal Correlation Analysis (PCA), the result of the PCC is reconstructed to remove the redundant characteristic information. Then, the min–max character scaling algorithm is used to regularize all characters to speed up the training process. Finally, a comparative validation of different models was performed on the NASA dataset, CALCE dataset, and MIT dataset. The results indicate that SOH and RUL can be predicted with an average root mean square error (RMSE) within 1.5%.
{"title":"SOH evaluation and RUL estimation of lithium-ion batteries based on MC-CNN-TimesNet model","authors":"Yanming Li , Xiaojuan Qin , Min Chai , Haoran Wu , Fujing Zhang , Fenghe Jiang , Changbao Wen","doi":"10.1016/j.ress.2025.111125","DOIUrl":"10.1016/j.ress.2025.111125","url":null,"abstract":"<div><div>Due to the increasing interest in the security of the battery system, precise and rapid work on the state of health (SOH) and remaining useful life (RUL) evaluation of lithium batteries (LIBs) is necessary in practice. In this article, a MC-CNN-TimesNet model is proposed to predict the SOH and RUL of lithium batteries. This model captures the deep-state characteristics of lithium battery aging by capturing the dependencies within and between different time scales. In addition, a Tree-structured Parzen Estimation (TPE) algorithm is used in the optimization of model parameters. In this study, we also conducted correlation analysis by Pearson Correlation Coefficient (PCC) on the input voltage, current, temperature, time, and capacity data to select the features with higher correlation with SOH and RUL. Based on the Principal Correlation Analysis (PCA), the result of the PCC is reconstructed to remove the redundant characteristic information. Then, the min–max character scaling algorithm is used to regularize all characters to speed up the training process. Finally, a comparative validation of different models was performed on the NASA dataset, CALCE dataset, and MIT dataset. The results indicate that SOH and RUL can be predicted with an average root mean square error (RMSE) within 1.5%.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111125"},"PeriodicalIF":9.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863582","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}
Pub Date : 2025-04-21DOI: 10.1016/j.ress.2025.111145
Xinxing Guo , Song Huang , Jianguo Wu , Chao Wang
Accurate prediction of remaining useful life (RUL) of a unit plays a critical role in condition-based maintenance, especially for hard failure cases. In industrial practice, due to differences in units’ types and working environments, there may exist multiple populations, and even within the same population, there are also variations among units. However, existing methods either assume that different units share the same population characteristics and ignore the between-population variations, or solely focus on between-population knowledge transfer while neglecting the within-population variations. To address this issue, this article proposes a transfer learning approach by integrating a Cox Proportional Hazards (PH) model with a Bayesian hierarchical model, which considers both within and between population variations. Specifically, a shared prior distribution is deployed to the parameters of the Cox model in each population, which builds the foundation for transfer learning across different populations. To model within-population variations, a linear mixed-effects model is utilized to represent heterogeneous degradation data of each unit. The effectiveness of the proposed method is demonstrated and compared with various benchmarks through a simulation study and a case study of turbine engines.
{"title":"A transfer learning approach for remaining useful life prediction subject to hard failure considering within and between population variations","authors":"Xinxing Guo , Song Huang , Jianguo Wu , Chao Wang","doi":"10.1016/j.ress.2025.111145","DOIUrl":"10.1016/j.ress.2025.111145","url":null,"abstract":"<div><div>Accurate prediction of remaining useful life (RUL) of a unit plays a critical role in condition-based maintenance, especially for hard failure cases. In industrial practice, due to differences in units’ types and working environments, there may exist multiple populations, and even within the same population, there are also variations among units. However, existing methods either assume that different units share the same population characteristics and ignore the between-population variations, or solely focus on between-population knowledge transfer while neglecting the within-population variations. To address this issue, this article proposes a transfer learning approach by integrating a Cox Proportional Hazards (PH) model with a Bayesian hierarchical model, which considers both within and between population variations. Specifically, a shared prior distribution is deployed to the parameters of the Cox model in each population, which builds the foundation for transfer learning across different populations. To model within-population variations, a linear mixed-effects model is utilized to represent heterogeneous degradation data of each unit. The effectiveness of the proposed method is demonstrated and compared with various benchmarks through a simulation study and a case study of turbine engines.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111145"},"PeriodicalIF":9.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863599","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}
Pub Date : 2025-04-20DOI: 10.1016/j.ress.2025.111162
Xinlong Li , Shuai Jiang , Baojia Chen , Fafa Chen , Youjun An , Yan Ran , Genbao Zhang
Developing an appropriate maintenance and spare parts inventory strategy is an effective means to ensure the safe and reliable operation of the system. Currently, the joint optimization research of maintenance and spare parts inventory strategy for multi-unit systems neglects the reuse of the unit after maintenance, which wastes maintenance resources and increases maintenance costs. In addition, in order to facilitate modeling and calculation, time parameters related to preventive maintenance are ignored, and the differentiated effects of preventive maintenance on different types of unit failures are also not considered, so the strategy developed is not accurate. In this paper, considering the differential effects of preventive maintenance on different types of unit failures, maintenance strategies at unit-level are formulated first. Then, the costs involved are analyzed, the joint optimization model of imperfect opportunistic maintenance based on dynamic time window and spare parts inventory for multi-unit system is established, which takes into account the time parameters related to preventive maintenance and the reuse of the unit after maintenance. Furthermore, the solution algorithm of the model is designed. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of the proposed method.
{"title":"Joint optimization of imperfect preventive opportunistic maintenance and spare parts inventory for multi-unit systems considering spare parts reuse","authors":"Xinlong Li , Shuai Jiang , Baojia Chen , Fafa Chen , Youjun An , Yan Ran , Genbao Zhang","doi":"10.1016/j.ress.2025.111162","DOIUrl":"10.1016/j.ress.2025.111162","url":null,"abstract":"<div><div>Developing an appropriate maintenance and spare parts inventory strategy is an effective means to ensure the safe and reliable operation of the system. Currently, the joint optimization research of maintenance and spare parts inventory strategy for multi-unit systems neglects the reuse of the unit after maintenance, which wastes maintenance resources and increases maintenance costs. In addition, in order to facilitate modeling and calculation, time parameters related to preventive maintenance are ignored, and the differentiated effects of preventive maintenance on different types of unit failures are also not considered, so the strategy developed is not accurate. In this paper, considering the differential effects of preventive maintenance on different types of unit failures, maintenance strategies at unit-level are formulated first. Then, the costs involved are analyzed, the joint optimization model of imperfect opportunistic maintenance based on dynamic time window and spare parts inventory for multi-unit system is established, which takes into account the time parameters related to preventive maintenance and the reuse of the unit after maintenance. Furthermore, the solution algorithm of the model is designed. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111162"},"PeriodicalIF":9.4,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859869","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}
Pub Date : 2025-04-19DOI: 10.1016/j.ress.2025.111160
Yongming Wang , Zhoujing Ye , Xinran Jia , Huifang Liu , Guoqing Zhou , Linbing Wang
With the acceleration of global climate change and urbanization, cities are increasingly vulnerable to extreme rainfall and flooding disasters. Urban infrastructure, which is interconnected physically, geographically, and informationally, serves as a carrier for the propagation of disasters, amplifying their effects and exacerbating the overall system's vulnerability. This paper proposes a novel method for analyzing urban flood disaster chains, using cascading failures within critical urban infrastructure networks as a basis. The method first constructs extreme rainfall flood disaster scenarios for urban areas through numerical simulation, considering rainfall and hydrological conditions. Next, a network model is developed that encompasses key urban infrastructures, including electricity, transportation, and communication systems. The coupling mechanism of these three critical infrastructures is defined, considering their geographical and physical connections. By analyzing the failure modes and propagation pathways of these infrastructures under extreme rainfall scenarios, the method explains the nonlinear spatiotemporal evolution of flood disaster chains, from localized failures ("points") to broader network-wide disruptions ("lines"), and ultimately to extensive systemic failures ("planes"). Furthermore, the impact of protecting key nodes within the infrastructure on the spatiotemporal evolution of disaster chains is analyzed. This analysis demonstrates how safeguarding critical points can disrupt the disaster chain and mitigate the impacts of flooding, offering new perspectives and analytical tools for urban flood disaster management and emergency response strategies. The findings are significant for understanding the interdependencies within urban infrastructure and enhancing the disaster resilience of urban systems.
{"title":"Flood disaster chain deduction based on cascading failures in urban critical infrastructure","authors":"Yongming Wang , Zhoujing Ye , Xinran Jia , Huifang Liu , Guoqing Zhou , Linbing Wang","doi":"10.1016/j.ress.2025.111160","DOIUrl":"10.1016/j.ress.2025.111160","url":null,"abstract":"<div><div>With the acceleration of global climate change and urbanization, cities are increasingly vulnerable to extreme rainfall and flooding disasters. Urban infrastructure, which is interconnected physically, geographically, and informationally, serves as a carrier for the propagation of disasters, amplifying their effects and exacerbating the overall system's vulnerability. This paper proposes a novel method for analyzing urban flood disaster chains, using cascading failures within critical urban infrastructure networks as a basis. The method first constructs extreme rainfall flood disaster scenarios for urban areas through numerical simulation, considering rainfall and hydrological conditions. Next, a network model is developed that encompasses key urban infrastructures, including electricity, transportation, and communication systems. The coupling mechanism of these three critical infrastructures is defined, considering their geographical and physical connections. By analyzing the failure modes and propagation pathways of these infrastructures under extreme rainfall scenarios, the method explains the nonlinear spatiotemporal evolution of flood disaster chains, from localized failures (\"points\") to broader network-wide disruptions (\"lines\"), and ultimately to extensive systemic failures (\"planes\"). Furthermore, the impact of protecting key nodes within the infrastructure on the spatiotemporal evolution of disaster chains is analyzed. This analysis demonstrates how safeguarding critical points can disrupt the disaster chain and mitigate the impacts of flooding, offering new perspectives and analytical tools for urban flood disaster management and emergency response strategies. The findings are significant for understanding the interdependencies within urban infrastructure and enhancing the disaster resilience of urban systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111160"},"PeriodicalIF":9.4,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859867","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}
In predictive maintenance (PdM), inspection plays a key role since it provides information about system health to support maintenance decision-making. However, inspection may be costly, which is one of the bottlenecks of the PdM. To reduce the overall inspection cost, this paper proposes a dynamic inspection model that incorporates three levels of inspection quality: (1) Binary state inspection, which is the cheapest one, can only reveal the binary state of the system, whether functioning or not; (2) Imperfect degradation inspection, which incurs a higher cost, but can provide the system state and degradation level with uncertainty; and (3) Perfect degradation inspection, which is the most expensive one, can provide the true system’s degradation level. Accordingly, at each inspection epoch, the inspection level is selected based on the predictive reliability of the system. By dynamically selecting the inspection level at each inspection epoch, the overall inspection cost of the system can be reduced. In that way, a reliability prediction model considering three kinds of inspections is first proposed for inspection and maintenance decision-making. Then, a maintenance cost model is developed to find the optimal predictive inspection and maintenance policy. Finally, a case study on predictive maintenance of an injection molding machine is conducted to investigate the applicability and benefit of the proposed dynamic inspection model.
{"title":"A dynamic inspection model for predictive maintenance considering different degrees of inspection quality","authors":"Duc-Hanh Dinh , Phuc Do , Minh-Hien Bui , Phuoc-Vinh Dang","doi":"10.1016/j.ress.2025.111165","DOIUrl":"10.1016/j.ress.2025.111165","url":null,"abstract":"<div><div>In predictive maintenance (PdM), inspection plays a key role since it provides information about system health to support maintenance decision-making. However, inspection may be costly, which is one of the bottlenecks of the PdM. To reduce the overall inspection cost, this paper proposes a dynamic inspection model that incorporates three levels of inspection quality: (1) Binary state inspection, which is the cheapest one, can only reveal the binary state of the system, whether functioning or not; (2) Imperfect degradation inspection, which incurs a higher cost, but can provide the system state and degradation level with uncertainty; and (3) Perfect degradation inspection, which is the most expensive one, can provide the true system’s degradation level. Accordingly, at each inspection epoch, the inspection level is selected based on the predictive reliability of the system. By dynamically selecting the inspection level at each inspection epoch, the overall inspection cost of the system can be reduced. In that way, a reliability prediction model considering three kinds of inspections is first proposed for inspection and maintenance decision-making. Then, a maintenance cost model is developed to find the optimal predictive inspection and maintenance policy. Finally, a case study on predictive maintenance of an injection molding machine is conducted to investigate the applicability and benefit of the proposed dynamic inspection model.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111165"},"PeriodicalIF":9.4,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855729","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}
Pub Date : 2025-04-19DOI: 10.1016/j.ress.2025.111168
Zhongji Su , Zexi Hua , Yongchuan Tang , Lei Wang , Qingyuan Zhu
Reliability analysis of railway signal equipment is pivotal to ensuring railway transport safety; however, current research lacks a holistic grasp of the operation of station signal equipment. This paper addresses the comprehensive reliability of railway station signal outdoor systems and establishes a station-level reliability analysis method based on a multi-dimensional universal generating function. The proposed method converts the complex station signal topology into an end-to-end multi-layer topology structure, utilizing a universal generating function model to derive a reliability model for the complex station-level railway signal equipment system. Subsequently, the system reliability is used as the measurement function to calculate and analyze the Fussel-Vesely importance of each signal point device in the station. Finally, experiments at a railway station demonstrated that the method described in this paper could effectively characterize the overall reliability of railway station signal equipment and reveal the differences in the importance of each signal point equipment. Furthermore, the method provides a theoretical foundation for decision-making processes related to inspection activities by visually depicting the operational status of the station to on-site railway operation and maintenance personnel.
{"title":"A method for reliability analysis of railway signal equipment at the station level based on universal generating function","authors":"Zhongji Su , Zexi Hua , Yongchuan Tang , Lei Wang , Qingyuan Zhu","doi":"10.1016/j.ress.2025.111168","DOIUrl":"10.1016/j.ress.2025.111168","url":null,"abstract":"<div><div>Reliability analysis of railway signal equipment is pivotal to ensuring railway transport safety; however, current research lacks a holistic grasp of the operation of station signal equipment. This paper addresses the comprehensive reliability of railway station signal outdoor systems and establishes a station-level reliability analysis method based on a multi-dimensional universal generating function. The proposed method converts the complex station signal topology into an end-to-end multi-layer topology structure, utilizing a universal generating function model to derive a reliability model for the complex station-level railway signal equipment system. Subsequently, the system reliability is used as the measurement function to calculate and analyze the Fussel-Vesely importance of each signal point device in the station. Finally, experiments at a railway station demonstrated that the method described in this paper could effectively characterize the overall reliability of railway station signal equipment and reveal the differences in the importance of each signal point equipment. Furthermore, the method provides a theoretical foundation for decision-making processes related to inspection activities by visually depicting the operational status of the station to on-site railway operation and maintenance personnel.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111168"},"PeriodicalIF":9.4,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863586","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}
Pub Date : 2025-04-17DOI: 10.1016/j.ress.2025.111077
Yunqi Yan , Ying Chen , Zhengda Cui , Tannan Xiao
Urban power distribution grids featuring loopy topologies and integrated distributed generations pose significant challenges for efficient and precise resilience quantification against disruptive events. This paper presents a probabilistic resilience assessment framework tailored for such grids. Risk metrics grounded in loss of load probability (LOLP) and expected energy not served (EENS) are formulated to evaluate resilience across multiple temporal stages. A multi-source multi-terminal network reliability (MSMT-NR) modeling approach is proposed to characterize the stochastic impact of component failures on load point connectivity. A computationally efficient algorithm framework is developed for the inference of the MSMT-NR problem, comprising: (1) Derivation of analytical LOLP expressions for grid topologies exhibiting tree-like load subgraphs; (2) A deletion–contraction decomposition technique generating solvable tree subgraphs from arbitrary network structures; (3) A computational graph-based inference methodology enabling efficient MSMT-NR evaluation and automatic differentiation for sensitivity analysis of component importance measures. Strategies for enhancing scalability to large-scale grids are devised. Extensive case studies on a real-world 30,894-node distribution grid corroborate the efficiency and precision of the proposed approach.
{"title":"Probabilistic resilience assessment of urban distribution power grids by fast inference of multi-source multi-terminal network reliability","authors":"Yunqi Yan , Ying Chen , Zhengda Cui , Tannan Xiao","doi":"10.1016/j.ress.2025.111077","DOIUrl":"10.1016/j.ress.2025.111077","url":null,"abstract":"<div><div>Urban power distribution grids featuring loopy topologies and integrated distributed generations pose significant challenges for efficient and precise resilience quantification against disruptive events. This paper presents a probabilistic resilience assessment framework tailored for such grids. Risk metrics grounded in loss of load probability (LOLP) and expected energy not served (EENS) are formulated to evaluate resilience across multiple temporal stages. A multi-source multi-terminal network reliability (MSMT-NR) modeling approach is proposed to characterize the stochastic impact of component failures on load point connectivity. A computationally efficient algorithm framework is developed for the inference of the MSMT-NR problem, comprising: (1) Derivation of analytical LOLP expressions for grid topologies exhibiting tree-like load subgraphs; (2) A deletion–contraction decomposition technique generating solvable tree subgraphs from arbitrary network structures; (3) A computational graph-based inference methodology enabling efficient MSMT-NR evaluation and automatic differentiation for sensitivity analysis of component importance measures. Strategies for enhancing scalability to large-scale grids are devised. Extensive case studies on a real-world 30,894-node distribution grid corroborate the efficiency and precision of the proposed approach.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111077"},"PeriodicalIF":9.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855711","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}
Pub Date : 2025-04-17DOI: 10.1016/j.ress.2025.111067
Chiara Nardin , Stefano Marelli , Oreste S. Bursi , Bruno Sudret , Marco Broccardo
Recently, there has been increased interest in assessing the seismic fragility of industrial plants and process equipment. This is reflected in the growing number of studies, community-funded research projects and experimental campaigns on the matter. Nonetheless, the complexity of the problem and its inherent modelling, coupled with a general scarcity of available data on process equipment, has limited the development of risk assessment methods. In fact, these limitations have led to the creation of simplified and quick-to-run models. In this context, we propose an innovative framework for developing state-dependent fragility functions. This new methodology combines limited data with the power of metamodelling and statistical techniques, namely polynomial chaos expansions (PCE) and bootstrapping. Therefore, we validated the framework on a simplified and computationally efficient MDoF system endowed with Bouc–Wen hysteresis. Then, we tested it on a real nonstructural industrial process component. Specifically, we applied the state-dependent fragility framework to a critical vertical tank of a multicomponent full-scale 3D steel braced frame (BF). The seismic performance of the BF endowed with process components was captured by means of shake table campaign within the European SPIF project. Finally, we derived state-dependent fragility functions based on the combination of PCE and bootstrap at a greatly reduced computational cost.
{"title":"UQ state-dependent framework for seismic fragility assessment of industrial components","authors":"Chiara Nardin , Stefano Marelli , Oreste S. Bursi , Bruno Sudret , Marco Broccardo","doi":"10.1016/j.ress.2025.111067","DOIUrl":"10.1016/j.ress.2025.111067","url":null,"abstract":"<div><div>Recently, there has been increased interest in assessing the seismic fragility of industrial plants and process equipment. This is reflected in the growing number of studies, community-funded research projects and experimental campaigns on the matter. Nonetheless, the complexity of the problem and its inherent modelling, coupled with a general scarcity of available data on process equipment, has limited the development of risk assessment methods. In fact, these limitations have led to the creation of simplified and quick-to-run models. In this context, we propose an innovative framework for developing state-dependent fragility functions. This new methodology combines limited data with the power of metamodelling and statistical techniques, namely polynomial chaos expansions (PCE) and bootstrapping. Therefore, we validated the framework on a simplified and computationally efficient MDoF system endowed with Bouc–Wen hysteresis. Then, we tested it on a real nonstructural industrial process component. Specifically, we applied the state-dependent fragility framework to a critical vertical tank of a multicomponent full-scale 3D steel braced frame (BF). The seismic performance of the BF endowed with process components was captured by means of shake table campaign within the European SPIF project. Finally, we derived state-dependent fragility functions based on the combination of PCE and bootstrap at a greatly reduced computational cost.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111067"},"PeriodicalIF":9.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870268","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}
Pub Date : 2025-04-17DOI: 10.1016/j.ress.2025.111149
Daniel Caicedo , Igor Tomić , Shaghayegh Karimzadeh , Vasco Bernardo , Katrin Beyer , Paulo B. Lourenço
This paper presents a probabilistic seismic demand model (PSDM) as a relationship between intensity measures (IMs) and engineering demand parameters (EDPs) for the seismic assessment of two case studies resembling historical masonry buildings. The first one is representative of stiff monumental buildings, and the second of tall and slender masonry buildings. Both structures are modelled in the OpenSees software using three-dimensional macroelements that consider both the in-plane and out-of-plane response of masonry walls. A set of 100 accelerograms are selected to represent the seismic excitation. After full characterization of the seismic input in terms of IMs, both buildings are subjected to the action of these accelerograms to study the maximum structural response in the context of cloud analysis. The most suitable IMs are determined subsequently under the notions of efficiency, practicability, proficiency, and sufficiency. In addition, a composed measure is proposed as a linear combination in logarithmic space of the IMs that exhibit the best coefficient of determination (R2) within the EDP vs. IM regression. This optimal composed measure is determined through machine learning-based Lasso regression. In the final stage of the study, fragility curves are derived to measure the likelihood of exceedance of certain levels of average roof displacement in terms of IM parameters.
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