Reliability Engineering & System Safety最新文献

{"title":"Remaining useful life prediction using a hybrid transfer learning-based adaptive Wiener process model","authors":"Xiaowu Chen ,&nbsp;Zhen Liu ,&nbsp;Kunping Wu ,&nbsp;Hanmin Sheng ,&nbsp;Yuhua Cheng","doi":"10.1016/j.ress.2025.110975","DOIUrl":"10.1016/j.ress.2025.110975","url":null,"abstract":"<div><div>Because of the characteristics of uncertainty description and interpretability, Wiener process (WP) has found extensive application in the domain of forecasting remaining useful life (RUL). Nevertheless, most existing WP often require selecting the suitable deterioration function and drift coefficient types based on the deterioration characteristics of target sample, which greatly limits their universality and feasibility in practical engineering. In order to address this issue, a hybrid adaptive WP based on transfer learning is presented to dynamically model the deterioration process of products with different deterioration features. The Brownian motion-based drift coefficient is applied to improve the adaptive characteristics of WP on the time-variant deterioration rate. A transfer learning-based long short-term memory (LSTM) model is utilized to adaptively track the dynamic nonlinear characteristics. According to the notion of first arrival time, we have successfully derived the explicit formula for the probability density function, so that the uncertainty contained in predicted results can be directly characterized. By using two capacity datasets and one torque bar deterioration dataset exhibiting distinct deterioration features, comparative experiments with eight different existing models have proven the universality and superiority of our model in forecasting RUL.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110975"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592798","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}

{"title":"A credible interval model updating method for structural population analysis and design stages based on small samples","authors":"Yang Cao,&nbsp;Xiaojun Wang","doi":"10.1016/j.ress.2025.110996","DOIUrl":"10.1016/j.ress.2025.110996","url":null,"abstract":"<div><div>In practical engineering, a persistent discrepancy exists between numerical simulations and real responses. This gap significantly undermines reliability in the established models and spurs the development of model updating. Yet, during the structural analysis and design phases, the focus of model updating often extends beyond the current structure to encompass the same type of structural population, so this paper proposes a credible interval model updating method for addressing the issue of uncertain model updating. This method divides the uncertain model updating problem into two subgoals: ensuring that the experimental responses credibly describe the real responses and that the simulation responses accurately fit experimental responses. For the first subgoal, the non-probabilistic credible convex sets for multi-type responses are established by introducing the concepts of multidimensional response space and credibility level. For the second subgoal, this paper categorizes model parameters into uncertain parameters and updating parameters, allowing the simulation model to fully consider prior information and be more generally applicable to the uncertain conditions of structural population. Particularly, the comparison between the predictions of the updated model and experimental results from other operating conditions highlights the robustness of the updated model and the advancement of the methodology.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110996"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628421","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}

{"title":"Efficient global reliability sensitivity method by combining dimensional reduction integral with stochastic collocation","authors":"Xiaomin Wu,&nbsp;Zhenzhou Lu","doi":"10.1016/j.ress.2025.110993","DOIUrl":"10.1016/j.ress.2025.110993","url":null,"abstract":"<div><div>Defined as the mean square difference between unconditional failure probability (FP) and conditional FP on fixed input realization, global reliability sensitivity (GRS) can quantify the effect of random input on FP. For efficiently estimating the GRS, a novel method is proposed by combining truncated <strong>d</strong>imensional <strong>r</strong>eduction <strong>i</strong>ntegral with <strong>s</strong>tochastic <strong>c</strong>ollocation (DRI-SC). In the DRI-SC, the unconditional and conditional FPs are equivalently converted into the expected cumulative distribution function (CDF) of a selected reduction input. Then, using the continuity of CDF, a truncated DRI is combined with SC to efficiently estimate the expected CDF. To further enhance the efficiency of DRI-SC, an adaptive Kriging model is trained to provide the integrand CDF values at the SC nodes. The novelties of the DRI-SC include deriving the unconditional and conditional FPs required by GRS as the expected CDF, designing an SC node-sharing strategy, and training the Kriging model in the SC node set. DRI-SC inherits the universality of numerical simulation but avoids its prohibitive computation, and the DRI-SC maintains the efficiency of the existing SC-based GRS methods but avoids the density fitting. The superiority of the DRI-SC over existing methods is verified by the presented examples.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110993"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578813","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}

{"title":"Enhanced risk assessment framework for complex maritime traffic systems via data driven: A case study of ship navigation in Arctic","authors":"Shenping Hu ,&nbsp;Cuiwen Fang ,&nbsp;Jianjun Wu ,&nbsp;Cunlong Fan ,&nbsp;Xinxin Zhang ,&nbsp;Xue Yang ,&nbsp;Bing Han","doi":"10.1016/j.ress.2025.110991","DOIUrl":"10.1016/j.ress.2025.110991","url":null,"abstract":"<div><div>The era of big data has been characterized by an increasing diversity of information and a deeper application of system safety. In this context, this study proposes an enhanced risk assessment (ERA) framework to estimate traffic risk from massive data obtained in complex maritime traffic systems. The ERA framework adopts a 4R model that includes risk perception, risk cognition, risk reasoning, and risk control. The ERA framework integrates the Systems Theoretic Accident Model and Process and Stochastic Petri Nets to analyze the ship traffic process and develop risk control schemes. The feasibility of the proposed framework is demonstrated by a case study in Arctic waters. The results indicate that ice concentration represents a key factor for ship traffic in Arctic waters and that the risk control scheme type is related to the ice resistance level of ships. Accordingly, for ships with low ice resistance or no ice-class ships, the traffic risk is high when they are passing through the East Siberian, Laptev, Kara Sea, and the Vilkitskogo Strait, and icebreakers are required in July and October. In contrast, for ships with a higher ice resistance, regular traffic is generally possible for the East Siberian and Laptev Seas.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110991"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563191","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}

{"title":"DCAGGCN: A novel method for remaining useful life prediction of bearings","authors":"Deqiang He ,&nbsp;Jiayang Zhao ,&nbsp;Zhenzhen Jin ,&nbsp;Chenggeng Huang ,&nbsp;Cai Yi ,&nbsp;Jinxin Wu","doi":"10.1016/j.ress.2025.110978","DOIUrl":"10.1016/j.ress.2025.110978","url":null,"abstract":"<div><div>Accurate prediction of Bearings' remaining useful life (RUL) is crucial in equipment operation and maintenance. The bearing RUL prediction technology based on GCN has recently been widely used. However, the existing GCN-based RUL prediction results are limited by two aspects : (1) GCN usually uses the predefined adjacency matrix to define the graph, which makes the graph unable to track the real-time correlation of degradation features in time. (2) Existing GCN uses only one to two layers of graph convolution and cannot extract deep features. Based on the issues above, this paper proposes a bearing RUL prediction model that utilizes a Dual-correlation adaptive gated graph convolutional network (DCAGGCN). Firstly, a predefined double correlation graph is proposed and obtained by feature channel data. Next, an adaptive graph is created by transforming a source matrix and a target matrix, and then integrating it with a predefined graph. This allows the network to consider two types of correlation and adaptively adjust the graph's topology. In addition, this paper proposes a gated convolution layer, which can greatly alleviate the over-smoothing problem caused by the stacking of graph convolution layers. The effectiveness of the proposed method is verified by two public datasets.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110978"},"PeriodicalIF":9.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578816","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}

{"title":"Efficient estimation of natural gas leakage source terms using physical information and improved particle filtering","authors":"Qi Jing ,&nbsp;Xingwang Song ,&nbsp;Bingcai Sun ,&nbsp;Yuntao Li ,&nbsp;Laibin Zhang","doi":"10.1016/j.ress.2025.110989","DOIUrl":"10.1016/j.ress.2025.110989","url":null,"abstract":"<div><div>Natural gas pipeline leaks can cause fires or explosions, making quick and accurate leak source identification critical for emergency response. This study develops a natural gas pipeline leakage source inversion model, where a Proper Orthogonal Decomposition-Physics-Informed Neural Network (POD-PINN) is integrated as the gas forward diffusion model. The inversion model combines an improved particle filtering algorithm, gas sensor data, and the POD-PINN, enabling rapid identification of leakage source terms. The gas source estimation results using POD-PINN and the Gaussian model as forward models were compared across different scenarios, and the impact of sensor errors on the inversion model was analyzed. Using POD-PINN as the forward model preserves accuracy while improving computational efficiency. The inclusion of a Gaussian kernel function and Markov Chain Monte Carlo (MCMC) method addresses degeneracy and impoverishment issues in standard particle filtering, preventing convergence to local optima. Results show that, across different scenarios, spatial position estimation errors are under 5%, and source strength errors are below 8%. When sensor measurement error is exceeds 0.5, the model cannot accurately estimate all source parameters. The proposed inversion model is subjected to convergence analysis, confirming its feasibility.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110989"},"PeriodicalIF":9.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619490","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}

{"title":"GE-MBAT: An efficient algorithm for reliability assessment in multi-state flow networks","authors":"Zhifeng Hao ,&nbsp;Wei-Chang Yeh","doi":"10.1016/j.ress.2025.110916","DOIUrl":"10.1016/j.ress.2025.110916","url":null,"abstract":"<div><div>Multi-state flow networks are increasingly critical across diverse applications such as network resilience, Internet of Things (IoT), and facility networks. These networks provide a more realistic representation of operational environments compared to binary-state models. Ensuring reliable network performance is crucial for the continuous and effective operation of these multi-state flow networks, especially as they grow in complexity. However, assessing reliability presents significant challenges due to the computational complexity involved. This paper introduces the \"Greater than or Equal to\" Multi-State Binary-Addition-Tree (GE-MBAT), designed to identify all vectors <em>X</em> of which (the maximum flow in the subgraph resulting from <em>X</em>) ≥ <em>d</em> rather than generating all possible multi-state vectors to enhance the efficiency and accuracy of reliability calculations in multi-state networks. The GE-MBAT reduces the generation of infeasible vectors, outperforming traditional methods in computational efficiency. This research contributes to the development of more reliable and robust network systems, with significant implications for critical infrastructure and advanced network technologies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110916"},"PeriodicalIF":9.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578811","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}

{"title":"Enhancing resilience of unmanned autonomous swarms through game theory-based cooperative reconfiguration","authors":"Chengxing Wu ,&nbsp;Hongzhong Deng ,&nbsp;Hongqian Wu ,&nbsp;Chengyi Tu","doi":"10.1016/j.ress.2025.110951","DOIUrl":"10.1016/j.ress.2025.110951","url":null,"abstract":"<div><div>The resilience of unmanned autonomous swarms (UAS) is critical for their ability to adjust behaviors and maintain essential functions when errors and failures occur. While significant advancements have been made in enhancing UAS resilience, the potential to utilize their inherent self-organizing and self-restructuring capabilities for further improvement remains largely underexplored. In this study, we present a game theory-based reconfiguration framework for UAS, enabling dynamic adjustments to the swarm’s network structure through cooperative payoffs. Building on this framework, we propose a UAS resilience metric to quantify the swarm’s task performance under continuous disturbances, validated through a case study. Finally, our analysis of the optimal configurations for enhancing UAS resilience—considering payoff matrices, swarm composition, communication range, and network structure—provides actionable insights for UAS design. We find that an optimal agent configuration ratio exists that maximizes UAS resilience, with specific constraints established for this ratio. Additionally, while increasing the communication range improves resilience, the benefits diminish beyond a certain threshold. We also find that network topology significantly impacts UAS resilience, particularly in structures with short global paths, which exhibit greater resilience.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110951"},"PeriodicalIF":9.4,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578812","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}

{"title":"A comparative assessment of domino accident analysis methods in process industries using LMAW and DNMA techniques","authors":"Sarbast Moslem ,&nbsp;Kamran Gholamizadeh ,&nbsp;Esmaeil Zarei ,&nbsp;Hans J Pasman ,&nbsp;Beatriz Martinez-Pastor ,&nbsp;Francesco Pilla","doi":"10.1016/j.ress.2025.110981","DOIUrl":"10.1016/j.ress.2025.110981","url":null,"abstract":"<div><div>Investigating domino incidents in process industries is critical for enhancing safety and preventing cascading failures with potentially severe consequences. A review of existing accident investigative methods underscores the need for appropriate criteria and methodologies, ensuring comprehensive analysis, and thus effective prevention possibility. This study addresses this need by evaluating, comparing, and ranking the effectiveness of various investigative methods. Ranking techniques of alternatives are many and show a steady improvement trend. This research applies the latest: the Logarithmic Methodology of Additive Weights (LMAW) to assign importance weights to relevant criteria. It then utilizes the Double Normalization-Based Multiple Aggregation (DNMA) technique to evaluate and rank the methods. Based on comparisons and sensitivity analysis this dual approach ensures a robust and objective assessment of the methods used in accident analysis. The criteria of applicability, accuracy, and comprehensiveness were given the highest weights based on expert judgments. AcciMap emerged as the most effective among the methods assessed, demonstrating superior performance in various aspects of accident analysis, followed by CAST and FRAM. AcciMap achieved the top ranking, exhibiting the highest overall effectiveness. These findings offer guidance for selecting accident analysis methods, aiding managers and safety practitioners in process industries. By leveraging these insights, organizations can make informed decisions on the most suitable methods for investigating domino incidents, thereby improving safety measures and response strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110981"},"PeriodicalIF":9.4,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578814","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}

{"title":"Prescribing optimal health-aware operation for urban air mobility with deep reinforcement learning","authors":"Mina Montazeri ,&nbsp;Chetan S. Kulkarni ,&nbsp;Olga Fink","doi":"10.1016/j.ress.2025.110897","DOIUrl":"10.1016/j.ress.2025.110897","url":null,"abstract":"<div><div>Urban Air Mobility (UAM) aims to expand existing transportation networks in metropolitan areas by offering short flights either to transport passengers or cargo. Electric vertical takeoff and landing aircraft powered by lithium-ion battery packs are considered promising for such applications. Efficient mission planning is crucial, maximizing the number of flights per battery charge while ensuring completion even under unforeseen events. As batteries degrade, precise mission planning becomes challenging due to uncertainties in the end-of-discharge prediction. This often leads to adding safety margins, reducing the number or duration of potential flights on one battery charge. While predicting the end of discharge can support decision-making, it remains insufficient in case of unforeseen events, such as adverse weather conditions. This necessitates health-aware real-time control to address any unexpected events and extend the time until the end of charge while taking the current degradation state into account. This paper addresses the joint problem of mission planning and health-aware real-time control of operational parameters to prescriptively control the duration of one discharge cycle of the battery pack. We propose an algorithm that proactively prescribes operational parameters to extend the discharge cycle based on the battery’s current health status while optimizing the mission. The proposed deep reinforcement learning algorithm facilitates operational parameter optimization and path planning while accounting for the degradation state, even in the presence of uncertainties. Evaluation of simulated flights of a National Aeronautics and Space Administration (NASA) conceptual multirotor aircraft model, collected from Hardware-in-the-loop experiments, demonstrates the algorithm’s near-optimal performance across various operational scenarios, allowing adaptation to changed environmental conditions. The proposed health-aware prescriptive algorithm enables a more flexible and efficient operation not only in single aircraft but also in fleet operations, increasing the overall system throughput.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110897"},"PeriodicalIF":9.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511315","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}