Pub Date : 2024-11-26DOI: 10.1016/j.ress.2024.110668
Zhe Wang , Lechang Yang , Xiaolei Fang , Hanxiao Zhang , Min Xie
Degradation profoundly affects the performance of industrial systems, necessitating operational safety prognostics. However, the availability of run-to-failure data is often limited, and labels in real-world scenarios are scarce. To address the challenge, this work utilizes the simulation domain to extract degradation knowledge and then adaptively transfers this knowledge to the experimental domain, aiming at estimating the remaining useful life (RUL). The relative RUL in the simulation domain is adopted, focusing on the degradation trend and avoiding the determination of absolute RUL. The feature disentanglement technique captures degradation-relevant features. To improve model performance, Bayesian optimization is introduced to search for optimal hyperparameters, and a two-task learning approach is designed to achieve the objectives of both domains. A few labeled experimental samples are used to adjust the predictor to appropriate scale. The case study on infrared degradation image streams validates the effectiveness of this domain adaptation scheme. Further analysis and discussions demonstrate the superiority of the model and the associated optimization strategy.
{"title":"Image-based remaining useful life prediction through adaptation from simulation to experimental domain","authors":"Zhe Wang , Lechang Yang , Xiaolei Fang , Hanxiao Zhang , Min Xie","doi":"10.1016/j.ress.2024.110668","DOIUrl":"10.1016/j.ress.2024.110668","url":null,"abstract":"<div><div>Degradation profoundly affects the performance of industrial systems, necessitating operational safety prognostics. However, the availability of run-to-failure data is often limited, and labels in real-world scenarios are scarce. To address the challenge, this work utilizes the simulation domain to extract degradation knowledge and then adaptively transfers this knowledge to the experimental domain, aiming at estimating the remaining useful life (RUL). The relative RUL in the simulation domain is adopted, focusing on the degradation trend and avoiding the determination of absolute RUL. The feature disentanglement technique captures degradation-relevant features. To improve model performance, Bayesian optimization is introduced to search for optimal hyperparameters, and a two-task learning approach is designed to achieve the objectives of both domains. A few labeled experimental samples are used to adjust the predictor to appropriate scale. The case study on infrared degradation image streams validates the effectiveness of this domain adaptation scheme. Further analysis and discussions demonstrate the superiority of the model and the associated optimization strategy.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110668"},"PeriodicalIF":9.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744545","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 : 2024-11-26DOI: 10.1016/j.ress.2024.110684
Zihao Lei , Feiyu Tian , Yu Su , Guangrui Wen , Ke Feng , Xuefeng Chen , Michael Beer , Chunsheng Yang
In recent years, unsupervised domain adaptation (UDA) has gained widespread application in addressing intelligent fault diagnosis under variable operating conditions. However, how to effectively model data structure information and integrate it into UDA has hindered the application of intelligent fault diagnosis in the industry. To solve this issue, a multi-scale and multi-structure information-embedded unsupervised graph transfer network for fault diagnosis is proposed. Specifically, a novel node feature extractor is first designed for feature embedding. To better fuse multi-scale information and obtain more effective features, a multi-scale convolutional layer and a hybrid attention module are utilized. Secondly, an adaptive similarity graph-constructing method based on the inner-product kernel is adopted to convert the node features into graph data. Next, the graph neural network (GNN) is introduced to obtain graph-structured information. Finally, a joint domain adaptation module is designed to cope with the covariance drift problem in cross-domain fault diagnosis. The proposed method exhibited state-of-the-art performance in the experiments of three case studies.
{"title":"Unsupervised graph transfer network with hybrid attention mechanism for fault diagnosis under variable operating conditions","authors":"Zihao Lei , Feiyu Tian , Yu Su , Guangrui Wen , Ke Feng , Xuefeng Chen , Michael Beer , Chunsheng Yang","doi":"10.1016/j.ress.2024.110684","DOIUrl":"10.1016/j.ress.2024.110684","url":null,"abstract":"<div><div>In recent years, unsupervised domain adaptation (UDA) has gained widespread application in addressing intelligent fault diagnosis under variable operating conditions. However, how to effectively model data structure information and integrate it into UDA has hindered the application of intelligent fault diagnosis in the industry. To solve this issue, a multi-scale and multi-structure information-embedded unsupervised graph transfer network for fault diagnosis is proposed. Specifically, a novel node feature extractor is first designed for feature embedding. To better fuse multi-scale information and obtain more effective features, a multi-scale convolutional layer and a hybrid attention module are utilized. Secondly, an adaptive similarity graph-constructing method based on the inner-product kernel is adopted to convert the node features into graph data. Next, the graph neural network (GNN) is introduced to obtain graph-structured information. Finally, a joint domain adaptation module is designed to cope with the covariance drift problem in cross-domain fault diagnosis. The proposed method exhibited state-of-the-art performance in the experiments of three case studies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110684"},"PeriodicalIF":9.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744546","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 : 2024-11-26DOI: 10.1016/j.ress.2024.110656
Ramin Talebi Khameneh , Kash Barker , Jose Emmanuel Ramirez-Marquez
Transportation infrastructure networks are prone to disruptions, most of which are beyond control. However, the spread of disinformation can worsen downtime in these systems by indirectly causing disruptions, such as station closures or rerouting of services based on false reports. The relationship between disinformation and the service disruptions is very important with reference to enhancing the resilience of transportation systems. This paper contributes to the field by applying artificial intelligence techniques to analyze how disinformation impacts service disruptions, particularly focusing on the Port Authority Trans-Hudson (PATH) system in New Jersey and New York, providing insights for improving operational responsiveness. The disruption operational impacts of disinformation are analyzed using several data sources, including schedules, ridership reports, and real-time alerts. A machine learning-based -means algorithm framework is applied to cluster disruption alerts from social media. Disruption scenarios dominated by disinformation are identified using advanced natural language processing (NLP) methods, specifically BERTopic and Latent Dirichlet Allocation (LDA) topic modeling techniques. A Monte Carlo simulation is applied to quantify the effects of this dominant disinformation-induced disruption scenario on the commuter time and costs. This study reveals that disinformation significantly influences infrastructure reliability and points out the necessity for effective strategies to combat its impacts. The findings reveal the importance of transportation disruptions to the functioning of the transportation system and emphasize the need for robust measures to reduce the adverse effects, hence making the system to be more resilient and secure in the public’s perception.
{"title":"A hybrid machine learning and simulation framework for modeling and understanding disinformation-induced disruptions in public transit systems","authors":"Ramin Talebi Khameneh , Kash Barker , Jose Emmanuel Ramirez-Marquez","doi":"10.1016/j.ress.2024.110656","DOIUrl":"10.1016/j.ress.2024.110656","url":null,"abstract":"<div><div>Transportation infrastructure networks are prone to disruptions, most of which are beyond control. However, the spread of disinformation can worsen downtime in these systems by indirectly causing disruptions, such as station closures or rerouting of services based on false reports. The relationship between disinformation and the service disruptions is very important with reference to enhancing the resilience of transportation systems. This paper contributes to the field by applying artificial intelligence techniques to analyze how disinformation impacts service disruptions, particularly focusing on the Port Authority Trans-Hudson (PATH) system in New Jersey and New York, providing insights for improving operational responsiveness. The disruption operational impacts of disinformation are analyzed using several data sources, including schedules, ridership reports, and real-time alerts. A machine learning-based <span><math><mi>K</mi></math></span>-means algorithm framework is applied to cluster disruption alerts from social media. Disruption scenarios dominated by disinformation are identified using advanced natural language processing (NLP) methods, specifically BERTopic and Latent Dirichlet Allocation (LDA) topic modeling techniques. A Monte Carlo simulation is applied to quantify the effects of this dominant disinformation-induced disruption scenario on the commuter time and costs. This study reveals that disinformation significantly influences infrastructure reliability and points out the necessity for effective strategies to combat its impacts. The findings reveal the importance of transportation disruptions to the functioning of the transportation system and emphasize the need for robust measures to reduce the adverse effects, hence making the system to be more resilient and secure in the public’s perception.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110656"},"PeriodicalIF":9.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744676","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 : 2024-11-25DOI: 10.1016/j.ress.2024.110675
Guanyi Liu , Shifeng Liu , Xuewei Li , Xueyan Li , Daqing Gong
Railway emergencies exhibit uncertainty and complex evolutionary processes during their development. Scenario deduction analysis plays a critical role in identifying the progression of railway emergencies, which is essential for effective response. This paper adopts a “scenario‒response” decision-making approach and proposes a multiscenario deduction model based on knowledge metatheory and dynamic Bayesian networks. First, through an in-depth analysis of railway accident cases, a scenario knowledge metarepresentation model is constructed on the basis of knowledge metatheory. On this basis, a scenario deduction model based on dynamic Bayesian networks is constructed, which is capable of analyzing the evolutionary trajectories of various scenarios. Additionally, an evidence conflict calculation method based on the Tanimoto measure is proposed to reduce the subjectivity of expert evaluations. Finally, the empirical part of this study focuses on a case analysis of a train derailment accident, with the experimental results demonstrating the effectiveness of the proposed model. Furthermore, this study validates the feasibility and utility of the proposed methods, providing valuable insights and guidance for enhancing railway operational safety.
{"title":"Multiscenario deduction analysis for railway emergencies using knowledge metatheory and dynamic Bayesian networks","authors":"Guanyi Liu , Shifeng Liu , Xuewei Li , Xueyan Li , Daqing Gong","doi":"10.1016/j.ress.2024.110675","DOIUrl":"10.1016/j.ress.2024.110675","url":null,"abstract":"<div><div>Railway emergencies exhibit uncertainty and complex evolutionary processes during their development. Scenario deduction analysis plays a critical role in identifying the progression of railway emergencies, which is essential for effective response. This paper adopts a “scenario‒response” decision-making approach and proposes a multiscenario deduction model based on knowledge metatheory and dynamic Bayesian networks. First, through an in-depth analysis of railway accident cases, a scenario knowledge metarepresentation model is constructed on the basis of knowledge metatheory. On this basis, a scenario deduction model based on dynamic Bayesian networks is constructed, which is capable of analyzing the evolutionary trajectories of various scenarios. Additionally, an evidence conflict calculation method based on the Tanimoto measure is proposed to reduce the subjectivity of expert evaluations. Finally, the empirical part of this study focuses on a case analysis of a train derailment accident, with the experimental results demonstrating the effectiveness of the proposed model. Furthermore, this study validates the feasibility and utility of the proposed methods, providing valuable insights and guidance for enhancing railway operational safety.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110675"},"PeriodicalIF":9.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744677","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 : 2024-11-23DOI: 10.1016/j.ress.2024.110677
Dezun Zhao , Xiaofan Huang , Tianyang Wang , Lingli Cui
Time-frequency analysis (TFA) has attracted the attention of many scholars and engineers for analyzing nonstationary signals in the field of condition monitoring of rotating machinery. For complex mechanical equipment, measured signals always contain both harmonic and impulsive components, which presents a challenge for current TFA methods. To concurrently characterize harmonic and impulsive components, a novel TFA algorithm, called generalized reassigning transform (GRT) is developed in this paper. First, the time-frequency fusion extraction criterion (TFFEC), which includes time-frequency data fusion (TFDF) and time-frequency data extraction (TFDE), is designed to calculate time-frequency representation (TFR) from short-time Fourier transform (STFT) results under different window sizes, which improves time-frequency resolution and eliminates noise influence. Furthermore, the chirp rate (CR)-based postprocessing strategy is constructed to characterize nonstationary signals with both harmonic-like and impulsive-like components. Specifically, the CR discrimination criterion is introduced to classify the TFFEC result into two distinct types: harmonic-like component and impulsive-like component, and then, the TFR with high energy concentration and strong readability is obtained by advanced postprocessing TFA including the synchro-reassigning transform (SRT) and horizontal reassigning transform (HRT). The effectiveness of the GRT in condition monitoring and fault diagnosis is validated through numerical and experiment verification.
{"title":"Generalized reassigning transform: Algorithm and applications","authors":"Dezun Zhao , Xiaofan Huang , Tianyang Wang , Lingli Cui","doi":"10.1016/j.ress.2024.110677","DOIUrl":"10.1016/j.ress.2024.110677","url":null,"abstract":"<div><div>Time-frequency analysis (TFA) has attracted the attention of many scholars and engineers for analyzing nonstationary signals in the field of condition monitoring of rotating machinery. For complex mechanical equipment, measured signals always contain both harmonic and impulsive components, which presents a challenge for current TFA methods. To concurrently characterize harmonic and impulsive components, a novel TFA algorithm, called generalized reassigning transform (GRT) is developed in this paper. First, the time-frequency fusion extraction criterion (TFFEC), which includes time-frequency data fusion (TFDF) and time-frequency data extraction (TFDE), is designed to calculate time-frequency representation (TFR) from short-time Fourier transform (STFT) results under different window sizes, which improves time-frequency resolution and eliminates noise influence. Furthermore, the chirp rate (CR)-based postprocessing strategy is constructed to characterize nonstationary signals with both harmonic-like and impulsive-like components. Specifically, the CR discrimination criterion is introduced to classify the TFFEC result into two distinct types: harmonic-like component and impulsive-like component, and then, the TFR with high energy concentration and strong readability is obtained by advanced postprocessing TFA including the synchro-reassigning transform (SRT) and horizontal reassigning transform (HRT). The effectiveness of the GRT in condition monitoring and fault diagnosis is validated through numerical and experiment verification.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110677"},"PeriodicalIF":9.4,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744549","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 : 2024-11-23DOI: 10.1016/j.ress.2024.110661
Ji Zhang , Xiaohui Chen , Youjun An , Lin Zhang , Haohao Shi , Weigang Xu
Machine reliability and product quality are usually investigated independently in most traditional models. However, in the actual manufacturing process, machine reliability and product quality are commonly interdependent due to strong coupling between the machine and the product. In this paper, three joint optimization models for preventive maintenance (PM) and product quality improvement are developed for deteriorating manufacturing systems considering quality-reliability dependency. First, the degradation process of quality-related components (QRCs) is depicted by Gamma process, and product quality deviation is obtained by considering the quantitative relationship between the degradation of QRCs and product quality. Second, joint optimization policy of age-based maintenance and product quality improvement is proposed for deteriorating manufacturing systems with quality-reliability dependency. Third, above maintenance policy is extended to manufacturing systems processing jobs with random processing time, and then maintenance first (MF) and maintenance last (ML) policies are developed, respectively. Furthermore, we compare all of the maintenance models to determine which policy should be selected for manufacturing systems under specific conditions, and all theoretical discussions in this paper are analytically made. Finally, the effectiveness of the proposed joint policies is verified by a case study on a cylindrical gear housing machining process.
{"title":"Joint optimization of preventive maintenance and product quality improvement policies for deteriorating manufacturing systems with quality-reliability dependency","authors":"Ji Zhang , Xiaohui Chen , Youjun An , Lin Zhang , Haohao Shi , Weigang Xu","doi":"10.1016/j.ress.2024.110661","DOIUrl":"10.1016/j.ress.2024.110661","url":null,"abstract":"<div><div>Machine reliability and product quality are usually investigated independently in most traditional models. However, in the actual manufacturing process, machine reliability and product quality are commonly interdependent due to strong coupling between the machine and the product. In this paper, three joint optimization models for preventive maintenance (PM) and product quality improvement are developed for deteriorating manufacturing systems considering quality-reliability dependency. First, the degradation process of quality-related components (QRCs) is depicted by Gamma process, and product quality deviation is obtained by considering the quantitative relationship between the degradation of QRCs and product quality. Second, joint optimization policy of age-based maintenance and product quality improvement is proposed for deteriorating manufacturing systems with quality-reliability dependency. Third, above maintenance policy is extended to manufacturing systems processing jobs with random processing time, and then maintenance first (MF) and maintenance last (ML) policies are developed, respectively. Furthermore, we compare all of the maintenance models to determine which policy should be selected for manufacturing systems under specific conditions, and all theoretical discussions in this paper are analytically made. Finally, the effectiveness of the proposed joint policies is verified by a case study on a cylindrical gear housing machining process.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110661"},"PeriodicalIF":9.4,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744673","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 : 2024-11-22DOI: 10.1016/j.ress.2024.110645
Zhichao He , Yanhui Wang , Wanhua Sun , Yucheng Hao , Weifu Xia
This paper presents an innovative framework for optimizing maintenance strategy in complex electromechanical systems (CEMS), by mitigating over-maintenance and suboptimal resource allocation. This study proposes an optimized maintenance strategy based on opportunistic maintenance with reliability as the core. A topological network model is used to establish a model that forecasts system reliability from function and structure, resulting in the identification of critical components in the system. Subsequently, a maintenance model for mechanical, electrical and information components is proposed and a method for determining maintenance reliability thresholds is suggested. The research imposes constraints in component and system reliability, in constructing an opportunistic maintenance strategies model. The objective of this model was to minimize the maintenance cost. A case study on high-speed railway bogie system supports this strategy, effectively guaranteeing system reliability while reducing maintenance costs. Maintenance resources are allocated reasonably across various stages, thereby reducing wastage of resources.
{"title":"A proactive opportunistic maintenance decision model based on reliability in train systems","authors":"Zhichao He , Yanhui Wang , Wanhua Sun , Yucheng Hao , Weifu Xia","doi":"10.1016/j.ress.2024.110645","DOIUrl":"10.1016/j.ress.2024.110645","url":null,"abstract":"<div><div>This paper presents an innovative framework for optimizing maintenance strategy in complex electromechanical systems (CEMS), by mitigating over-maintenance and suboptimal resource allocation. This study proposes an optimized maintenance strategy based on opportunistic maintenance with reliability as the core. A topological network model is used to establish a model that forecasts system reliability from function and structure, resulting in the identification of critical components in the system. Subsequently, a maintenance model for mechanical, electrical and information components is proposed and a method for determining maintenance reliability thresholds is suggested. The research imposes constraints in component and system reliability, in constructing an opportunistic maintenance strategies model. The objective of this model was to minimize the maintenance cost. A case study on high-speed railway bogie system supports this strategy, effectively guaranteeing system reliability while reducing maintenance costs. Maintenance resources are allocated reasonably across various stages, thereby reducing wastage of resources.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110645"},"PeriodicalIF":9.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744669","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 : 2024-11-22DOI: 10.1016/j.ress.2024.110658
Shima Mohebbi , Babak Aslani , Mark Herman Dsouza
Critical infrastructure systems are governed by several sectors working together to maintain social, economic, and environmental well-being. Their cyber–physical interdependencies influence their performance and resilience to routine failures and extreme events. To balance investment and restoration decisions in the face of disruptive events, mostly centralized mathematical formulations and solutions were presented in the literature. However, not all physical and dynamic characteristics of infrastructure systems and their decision makers can be modeled via mathematical models. In this study, we take a different approach and utilize agent-based modeling to simulate city-scale interdependent infrastructure networks as a complex adaptive system. In specific, we design a flexible modular (object-oriented) simulation tool capable of capturing the dynamic behaviors of various networks. We first model each infrastructure as a weighted graph with relevant geospatial attributes. Decision makers for each infrastructure sector are represented by intelligent agents. We then define three information and coordination schemes among agents, including no communication, leader–follower, and decentralized coalitions. To show the applicability of the approach, we use publicly available interdependent water distribution and road networks for the City of Tampa, FL, which is prone to hurricanes. We simulate different magnitudes of physical, cyber, and cyber–physical failures, evaluate resource allocation decisions, made by agents under each coordination scheme, and quantify the aggregated resilience. The simulation platform will help municipalities in various cities to quantify the impact of their collective decision making and identify the best coordination structures when interdependencies are modeled in infrastructure systems.
{"title":"A graph-empowered agent-based simulation: Impacts of coordination schemes on critical infrastructures resilience","authors":"Shima Mohebbi , Babak Aslani , Mark Herman Dsouza","doi":"10.1016/j.ress.2024.110658","DOIUrl":"10.1016/j.ress.2024.110658","url":null,"abstract":"<div><div>Critical infrastructure systems are governed by several sectors working together to maintain social, economic, and environmental well-being. Their cyber–physical interdependencies influence their performance and resilience to routine failures and extreme events. To balance investment and restoration decisions in the face of disruptive events, mostly centralized mathematical formulations and solutions were presented in the literature. However, not all physical and dynamic characteristics of infrastructure systems and their decision makers can be modeled via mathematical models. In this study, we take a different approach and utilize agent-based modeling to simulate city-scale interdependent infrastructure networks as a complex adaptive system. In specific, we design a flexible modular (object-oriented) simulation tool capable of capturing the dynamic behaviors of various networks. We first model each infrastructure as a weighted graph with relevant geospatial attributes. Decision makers for each infrastructure sector are represented by intelligent agents. We then define three information and coordination schemes among agents, including no communication, leader–follower, and decentralized coalitions. To show the applicability of the approach, we use publicly available interdependent water distribution and road networks for the City of Tampa, FL, which is prone to hurricanes. We simulate different magnitudes of physical, cyber, and cyber–physical failures, evaluate resource allocation decisions, made by agents under each coordination scheme, and quantify the aggregated resilience. The simulation platform will help municipalities in various cities to quantify the impact of their collective decision making and identify the best coordination structures when interdependencies are modeled in infrastructure systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110658"},"PeriodicalIF":9.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744666","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 : 2024-11-22DOI: 10.1016/j.ress.2024.110631
Dheeraj Goyal , Maxim Finkelstein , Nil Kamal Hazra
For some repairable systems executing missions/tasks, a functional failure, i.e., a failure of a mission or task can occur not immediately after equipment failure but with some delay. This happens when a failure/defect is not repaired within some specified period of time. Alternatively, a functional failure can also occur when a new failure/defect happens relatively soon after the completion of the previous repair. In this paper, we present a new stochastic model that defines and describes the lifetimes of this kind of repairable systems with operational constraints. A new approach based on Laplace transforms is developed to study the reliability function and the mean time to failure for these systems. Furthermore, we consider the stochastic model when only a finite number of repairs are allowed and obtain relevant reliability indices for this case as well. Detailed numerical examples illustrate our findings.
{"title":"On repairable systems with time redundancy and operational constraints","authors":"Dheeraj Goyal , Maxim Finkelstein , Nil Kamal Hazra","doi":"10.1016/j.ress.2024.110631","DOIUrl":"10.1016/j.ress.2024.110631","url":null,"abstract":"<div><div>For some repairable systems executing missions/tasks, a functional failure, i.e., a failure of a mission or task can occur not immediately after equipment failure but with some delay. This happens when a failure/defect is not repaired within some specified period of time. Alternatively, a functional failure can also occur when a new failure/defect happens relatively soon after the completion of the previous repair. In this paper, we present a new stochastic model that defines and describes the lifetimes of this kind of repairable systems with operational constraints. A new approach based on Laplace transforms is developed to study the reliability function and the mean time to failure for these systems. Furthermore, we consider the stochastic model when only a finite number of repairs are allowed and obtain relevant reliability indices for this case as well. Detailed numerical examples illustrate our findings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110631"},"PeriodicalIF":9.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744671","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}
Pub Date : 2024-11-20DOI: 10.1016/j.ress.2024.110670
Matteo Valente, Federica Ricci, Valerio Cozzani
The threat of natural hazards and their devastating consequences is challenging the resilience of society. Especially in industrial areas, where relevant quantities of hazardous materials are handled, natural hazards may trigger severe technological accidents (Natech). Resilience Engineering (RE) principles have been recently introduced in the context of process safety and Natech accidents, aiming at the development of systems able to withstand and rapidly recover from unexpected events. A systematic review of the literature addressing the application of resilience drivers to the framework of Natech assessment and management was carried out, providing a critical evaluation of strategies, methods, and tools proposed for resilience to Natech events. The study focused on chemical and process industries and related sectors, as the petrochemical and energy industry, analysing both quantitative and qualitative approaches. The fundamental aspects necessary to develop methods and tools for the quantitative resilience assessment of industrial facilities facing natural hazards have been identified. These mainly address the phases of the resilience evolution process (REP), the features of Natech scenarios, and the resilience metrics. Gaps and limitations in the state of the art, deserving attention in future research, were identified and discussed. These include the development of a detailed framework for the REP, a comprehensive assessment of the post-accident phases, and the integration of specific features of Natech scenarios. Other important aspects identified are the need to bridge the gap between qualitative and quantitative methods and the importance of developing a multidimensional approach to achieve a comprehensive understanding and assessment of the resilience of facilities exposed to natural hazards.
{"title":"A systematic review of Resilience Engineering applications to Natech accidents in the chemical and process industry","authors":"Matteo Valente, Federica Ricci, Valerio Cozzani","doi":"10.1016/j.ress.2024.110670","DOIUrl":"10.1016/j.ress.2024.110670","url":null,"abstract":"<div><div>The threat of natural hazards and their devastating consequences is challenging the resilience of society. Especially in industrial areas, where relevant quantities of hazardous materials are handled, natural hazards may trigger severe technological accidents (Natech). Resilience Engineering (RE) principles have been recently introduced in the context of process safety and Natech accidents, aiming at the development of systems able to withstand and rapidly recover from unexpected events. A systematic review of the literature addressing the application of resilience drivers to the framework of Natech assessment and management was carried out, providing a critical evaluation of strategies, methods, and tools proposed for resilience to Natech events. The study focused on chemical and process industries and related sectors, as the petrochemical and energy industry, analysing both quantitative and qualitative approaches. The fundamental aspects necessary to develop methods and tools for the quantitative resilience assessment of industrial facilities facing natural hazards have been identified. These mainly address the phases of the resilience evolution process (REP), the features of Natech scenarios, and the resilience metrics. Gaps and limitations in the state of the art, deserving attention in future research, were identified and discussed. These include the development of a detailed framework for the REP, a comprehensive assessment of the post-accident phases, and the integration of specific features of Natech scenarios. Other important aspects identified are the need to bridge the gap between qualitative and quantitative methods and the importance of developing a multidimensional approach to achieve a comprehensive understanding and assessment of the resilience of facilities exposed to natural hazards.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110670"},"PeriodicalIF":9.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744552","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}
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