Reliability Engineering & System Safety最新文献_第2页

A systematic resilience assessment framework for multi-state systems based on physics-informed neural network

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-30 DOI: 10.1016/j.ress.2025.110866

Yuxuan He , Enrico Zio , Zhaoming Yang , Qi Xiang , Lin Fan , Qian He , Shiliang Peng , Zongjie Zhang , Huai Su , Jinjun Zhang

{"title":"A systematic resilience assessment framework for multi-state systems based on physics-informed neural network","authors":"Yuxuan He , Enrico Zio , Zhaoming Yang , Qi Xiang , Lin Fan , Qian He , Shiliang Peng , Zongjie Zhang , Huai Su , Jinjun Zhang","doi":"10.1016/j.ress.2025.110866","DOIUrl":"10.1016/j.ress.2025.110866","url":null,"abstract":"<div><div>Resilience is crucial for systems to maintain functionality under disturbances, especially in critical applications. However, current methods for assessing resilience in multi-state systems (MSS), particularly those modeled with Markov Repairable Processes (MRP), often face high computational costs and inefficiencies in handling complex dynamics. To address these issues, this paper proposes a systematic framework for resilience assessment of MSS whose recovery process is described as a MRP, integrated with enhanced Physics-Informed Neural Networks (PINN). In the first step of the framework, the computation of resilience indices is performed, based on the MRP of the MSS and considering the system evolution through vulnerable and recovery phases. In the second step of the framework, the enhanced PINN is integrated into the MRP solution. A typical standby MSS structure is analyzed based on the proposed framework. By gradient calibration and momentum-driving training, the computational cost is shown to be reduced by 92.4 %, compared to the eigenvector method of solution. The approach is adaptable to other safety-critical systems, offering a robust tool for more effective resilience evaluation and system optimization.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110866"},"PeriodicalIF":9.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143287507","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

Global assessment modeling to reveal spatiotemporal variations and socioenvironmental drivers in drainage system flood-resilient performance

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-30 DOI: 10.1016/j.ress.2025.110862

Zihan Liu, Yexin He, Hanbin Luo, Wenli Liu, Tianxiang Liu, Yongping Di

{"title":"Global assessment modeling to reveal spatiotemporal variations and socioenvironmental drivers in drainage system flood-resilient performance","authors":"Zihan Liu, Yexin He, Hanbin Luo, Wenli Liu, Tianxiang Liu, Yongping Di","doi":"10.1016/j.ress.2025.110862","DOIUrl":"10.1016/j.ress.2025.110862","url":null,"abstract":"<div><div>Flood resilience has received considerable attention from urban areas experiencing intense rainstorms due to urbanization and climate change. Preevent assessments and measures can facilitate the resilience performance of urban drainage infrastructure. Current resilience studies predominantly evaluate a single fixed scenario without exploring the spatiotemporal dynamic process and its underlying drivers. Here, a synthetic assessment model is developed to systematically evaluate the spatiotemporal dynamics of urban drainage system (UDS) resilience via a targeted failure pattern approach coupled with a component degradation model to identify resilience-sensitive pipe segments and provide quantitative simulation evaluations of global resilience performance at spatiotemporal scales. The Mantel test reveals the spatial correlation between pipeline sensitivity performance and socioenvironmental factors and identifies key indicators. We find that spatial failure patterns reduce system resilience more than component deterioration over time does. The resilience-sensitivity performance of pipes exhibits a significant spatial relationship with two indicators (i.e., nighttime light and population density). Subcatchment flooding-vulnerable performance is thus proven to be more sensitive to localized changes in two socioenvironmental drivers. This study provides a comprehensive framework for evaluating the spatiotemporal dynamics of urban drainage system resilience, which enables decision-makers to pinpoint priority zones where the deployment of flood mitigation strategies would be optimally effective.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110862"},"PeriodicalIF":9.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143287501","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

Natural gas pipeline weak leakage detection based on negative pressure wave decomposition and feature enhancement

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-29 DOI: 10.1016/j.ress.2025.110857

Lin Ye , Chengyou Wang , Xiao Zhou , Baocheng Jiang , Changsong Yu , Zhiliang Qin

{"title":"Natural gas pipeline weak leakage detection based on negative pressure wave decomposition and feature enhancement","authors":"Lin Ye , Chengyou Wang , Xiao Zhou , Baocheng Jiang , Changsong Yu , Zhiliang Qin","doi":"10.1016/j.ress.2025.110857","DOIUrl":"10.1016/j.ress.2025.110857","url":null,"abstract":"<div><div>Natural gas pipeline leakage detection (PLD) based on negative pressure wave (NPW) signals faces significant challenges, including external noise that obscures crucial information and inadequate feature extraction, which often result in low detection accuracy. To address these issues, a weak leakage detection model for natural gas pipelines, named MDDet, is proposed, which integrates variational mode decomposition (VMD)-based signal decomposition and feature enhancement. The MDDet consists of two main components. The first component is the mutual difference distance (MDD) algorithm, which processes NPW signals by integrating signal decomposition for denoising and selecting the optimal intrinsic mode function <span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>o</mi></mrow></msub></math></span> related to leakage information. The second component is the dual-stream enhanced feature (DEF) algorithm that uses data cropping and dimensionality enhancement to enhance feature for weak leakage detection. Field tests were conducted on natural gas supply systems in two cities to validate the model, with further evaluation of its efficiency in realistic urban pipeline environments in China. The results demonstrate that the MDD algorithm accurately extracts effective leakage information and the DEF algorithm effectively classifies multi-channel feature sample, reflecting the working conditions of the monitored pipelines.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110857"},"PeriodicalIF":9.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143353091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel approach for structural system reliability evaluation using decoupled first-order reliability method and equivalent extreme-value event

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-29 DOI: 10.1016/j.ress.2025.110851

Xin Chen , Jie Li

{"title":"A novel approach for structural system reliability evaluation using decoupled first-order reliability method and equivalent extreme-value event","authors":"Xin Chen , Jie Li","doi":"10.1016/j.ress.2025.110851","DOIUrl":"10.1016/j.ress.2025.110851","url":null,"abstract":"<div><div>The first-order reliability method (FORM) has been widely used in system reliability evaluation. However, calculating system reliability of structures with hundreds of components by FORM poses significant challenges. These difficulties arise because it requires determining multivariate normal integrals, which is generally impractical due to the high dimension of these integrals. Additionally, explicit expressions for the limit state functions (LSFs) of components cannot be generally obtained, leading to substantial computational costs for determining the gradients of LSFs. To address these issues, a novel approach called the equivalent extreme-value event-based decoupled FORM (EEVE-DFORM) is proposed. In EEVE-DFORM, the high-dimensional normal integrals are reduced to one-dimensional integrals of extreme value distributions according to the principle of equivalent extreme-value event (EEVE), and extreme value distributions are derived using the probability density evolution method (PDEM). In conjunction with a Galerkin-type stochastic finite element method (GSFEM), a decoupled FORM, where reliability computation is decoupled with finite element analysis, is developed to calculate the reliability of components with implicit LSFs. Five numerical examples are investigated to demonstrate the efficacy of the proposed methodology. The results indicate that the system reliability of series, parallel, and general structural systems can be accurately and efficiently determined using the proposed method, even when dealing with hundreds of components.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110851"},"PeriodicalIF":9.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143287506","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

High-dimensional points selection strategy for PDEM-based stochastic dynamic analysis of structures

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-29 DOI: 10.1016/j.ress.2025.110849

Jun Xu , Yang Zhang , Jie Li

{"title":"High-dimensional points selection strategy for PDEM-based stochastic dynamic analysis of structures","authors":"Jun Xu , Yang Zhang , Jie Li","doi":"10.1016/j.ress.2025.110849","DOIUrl":"10.1016/j.ress.2025.110849","url":null,"abstract":"<div><div>The Probability Density Evolution Method (PDEM) is effective for stochastic dynamic analysis of structures. Within PDEM, the selection of representative point sets and the computation of their assigned probabilities are critical for balancing accuracy and efficiency, particularly in high-dimensional scenarios. This paper presents a novel strategy for point selection tailored to these requirements. The strategy integrates a deterministic low-discrepancy point set with a Bayesian-based Assigned Probability (BAP) calculation procedure within the PDEM framework. Initially, a New Generating Vector-based Number-Theoretical Method (NGV-NTM) is developed to produce a high-dimensional low-discrepancy point set. This set is then transformed into the original random-variate space to form the representative point set. Subsequently, the BAP procedure calculates the assigned probabilities for the representative point set by employing a Gaussian model for the prior distribution and likelihood function, and estimating the posterior probabilities through solving a maximum a posteriori estimation problem. Once the assigned probabilities are obtained, the representative point set is rearranged to better align with the input distributions. This refined set is then used for the final PDEM-based analysis. The effectiveness of the proposed method is validated through two numerical examples, with results compared to those obtained using Monte Carlo simulations and Sobol sequences.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110849"},"PeriodicalIF":9.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143287502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey on few-shot learning for remaining useful life prediction

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-28 DOI: 10.1016/j.ress.2025.110850

Renpeng Mo , Han Zhou , Hongpeng Yin , Xiaosheng Si

{"title":"A survey on few-shot learning for remaining useful life prediction","authors":"Renpeng Mo , Han Zhou , Hongpeng Yin , Xiaosheng Si","doi":"10.1016/j.ress.2025.110850","DOIUrl":"10.1016/j.ress.2025.110850","url":null,"abstract":"<div><div>The prediction performance of most data-driven remaining useful life (RUL) prediction methods relies on sufficient training samples, which is challenging in few-shot scenarios such as time-consuming or expensive monitoring, and the lack of historical data for newer equipment. Therefore, utilizing few-shot learning (FSL) methods to accurately obtain mapping functions of equipment RUL from limited data has attracted the attention of many researchers. Despite this, a systematic review of this class of prediction methods is still lacking. To fill this gap, this review comprehensively examines numerous research findings on RUL prediction in few-shot scenarios, groups the existing FSL for RUL prediction methods into three categories based on different sources of prior knowledge, introduces the principles, and recent advances of each category in detail, and, in particular, highlights the impact and constraints of RUL prediction task characteristics on various FSL methods. Additionally, this review discusses the challenges faced by FSL-RUL prediction during development and application, and explores potential future opportunities from an informative and knowledge perspective.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110850"},"PeriodicalIF":9.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143287503","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

Maritime Near-Miss prediction framework and model interpretation analysis method based on Transformer neural network model with multi-task classification variables

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-28 DOI: 10.1016/j.ress.2025.110845

Pengxv Chen , Anmin Zhang , Shenwen Zhang , Taoning Dong , Xi Zeng , Shuai Chen , Peiru Shi , Yiik Diew Wong , Qingji Zhou

{"title":"Maritime Near-Miss prediction framework and model interpretation analysis method based on Transformer neural network model with multi-task classification variables","authors":"Pengxv Chen , Anmin Zhang , Shenwen Zhang , Taoning Dong , Xi Zeng , Shuai Chen , Peiru Shi , Yiik Diew Wong , Qingji Zhou","doi":"10.1016/j.ress.2025.110845","DOIUrl":"10.1016/j.ress.2025.110845","url":null,"abstract":"<div><div>The prediction and analysis of Maritime Near-Miss incidents are crucial for enhancing safety protocols and accidents. In this study, a Multi-task classification variant of the Transformer neural network model is presented, designed to predict and interpret Maritime Near-Miss data. Incident reports were collected and analyzed using maritime open source intelligence, and a multi-task model based on the Transformer neural network was developed. A framework for training structured and unstructured data to predict incident risk levels and the necessity to activate the Stop Work mechanism was built. The model incorporates BERT text classification and Multi-label synthesis minority oversampling techniques to improve feature representation and address class imbalance. Dynamic weights were used to balance the learning of the two tasks during training. Experimental results show excellent performance in both risk assessment and stop work prediction tasks. The model was interpreted using feature maps and game theory, providing a new tool for maritime safety management and offering valuable insights for risk assessment and decision-making.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110845"},"PeriodicalIF":9.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143353095","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

Optimal allocation of defensive resources in regional railway networks under intentional attacks

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-28 DOI: 10.1016/j.ress.2025.110864

Benwei Hou , Pengxu Chen , Xudong Zhao , Zhilong Chen

{"title":"Optimal allocation of defensive resources in regional railway networks under intentional attacks","authors":"Benwei Hou , Pengxu Chen , Xudong Zhao , Zhilong Chen","doi":"10.1016/j.ress.2025.110864","DOIUrl":"10.1016/j.ress.2025.110864","url":null,"abstract":"<div><div>Railway network is one of the busiest regional transportation infrastructures, which is exposed to a high risk of intentional attacks. Given the railway network stations have a larger service area, attackers may have different biases toward the valuation of railway stations or lines. This paper proposes a method for optimally allocating defensive resources based on a Bayesian game model and a comprehensive importance evaluation model of stations by multi-layer network models, aiming to reduce the losses of defenders. The attack strategy was made according to the importance of railway stations evaluated by three-layer network models, namely topology layer, the ridership layer and the travel time layer, which depict the features of railway networks and also reflect the variety of attacker's biases. The optimal allocation of defensive resources was obtained under the Nash equilibrium of Bayesian game. The proposed method is implemented in a regional railway network in north China, and the case network's risk under various attack strategies were compared to validate the applicability of this model. The application results show that the optimal defensive resources allocation based on the importance evaluation by three-layer models has the lowest risk considering the variety in the attacker's biases.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110864"},"PeriodicalIF":9.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349052","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

Joint optimization of maintenance and speed selection for transportation systems

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-28 DOI: 10.1016/j.ress.2025.110865

Xian Zhao , Zhenru Liu , Congshan Wu , Tongtong Jin

{"title":"Joint optimization of maintenance and speed selection for transportation systems","authors":"Xian Zhao , Zhenru Liu , Congshan Wu , Tongtong Jin","doi":"10.1016/j.ress.2025.110865","DOIUrl":"10.1016/j.ress.2025.110865","url":null,"abstract":"<div><div>There is an increasing demand for long-distance emergency transportation missions. Transportation systems often perform missions in harsh environments, and the valid shock probability varies when the system is shocked at different speed levels. System failure or excessively long transportation times can cause significant economic losses, so both successful completion and the shortest possible time are critical for emergency missions. Based on the above insights, this paper investigates the joint optimization of maintenance and speed selection for transportation systems in stochastic shock environments. The optimization goal is to minimize the total cost of system failure, maintenance, and operation, aiming to complete transportation missions with high reliability and in a short time. A Markov decision process is formulated to model the system operation process and obtain the optimal joint policy. For comparison, two heuristic policies are proposed. The effectiveness of the joint optimization policy to reduce the cost is verified by taking the UAV to perform an emergency mission as an example. The results show that under certain circumstances, the system has the opportunity to adjust its speed to control the risk of system failure.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110865"},"PeriodicalIF":9.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143351039","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 of weighted k-out-of-n: G performance sharing systems with multiple transmission loss levels

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-01-27 DOI: 10.1016/j.ress.2025.110859

Congshan Wu , Xian Zhao , Siqi Wang , Yanbo Song

{"title":"Reliability analysis of weighted k-out-of-n: G performance sharing systems with multiple transmission loss levels","authors":"Congshan Wu , Xian Zhao , Siqi Wang , Yanbo Song","doi":"10.1016/j.ress.2025.110859","DOIUrl":"10.1016/j.ress.2025.110859","url":null,"abstract":"<div><div>This paper constructs a reliability model for the weighted <em>k</em>-out-of-<em>n</em>: G performance sharing which suffers random shocks and transmission loss. The proposed system has a common bus connecting all the components. Each component contains random performance and demand, and the demand should be satisfied by the performance. The common bus is applied to share performances among components, and has random capacity. Note that the performance might be lost during transmission and that the loss rate is affected by shocks. External shocks can be classified into invalid shocks, valid shocks and extreme shocks. One component fails if its demand cannot be satisfied after performance sharing. Each component carries a weight according to its importance degree. The system works if the total weight of the working components is no less than <em>k</em>. A method that combines the Markov process method and the universal generating function technique is used to evaluate system reliability. Numerical examples are illustrated to demonstrate the effectiveness of the proposed model and method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110859"},"PeriodicalIF":9.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143286645","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