Reliability Engineering & System Safety最新文献

{"title":"Joint optimization of multi-stage work intensity selecting and maintenance policy for a two-dimensional balanced system","authors":"Siqi Wang ,&nbsp;Songqi Li ,&nbsp;Xian Zhao ,&nbsp;Congshan Wu","doi":"10.1016/j.ress.2025.111362","DOIUrl":"10.1016/j.ress.2025.111362","url":null,"abstract":"<div><div>Most existing research on balanced systems based on component state only considers the one-dimensional linear structure, but many engineering systems are two-dimensional. Driven by this reality, a two-dimensional balanced system reliability model is proposed. All components are arranged in a two-dimensional matrix, and divided into grids of equal size. The system is required to complete a task containing several phases. The system has several optional work intensities such that it can complete more tasks under a higher work intensity, but the failure rate also increases. When the maximum difference of component states in each grid is less than a predetermined threshold, the system is balanced. When the system is unbalanced, or the total number of failed components in a grid exceeds a limit, it fails. To complete more tasks and reduce the loss caused by system failure, a joint policy of work intensity selecting and maintenance is proposed. A Markov decision process is used to describe the system operation process. The optimal results are calculated by the value iteration algorithm. Finally, a two-dimensional manufacturing system is taken as an example to verify the effectiveness of the proposed joint strategy.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111362"},"PeriodicalIF":9.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365418","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 modified TimeGAN-based data augmentation approach for the state of health prediction of Lithium-Ion Batteries","authors":"Soufian Echabarri ,&nbsp;Phuc Do ,&nbsp;Hai-Canh Vu ,&nbsp;Pierre-Yves Liegeois","doi":"10.1016/j.ress.2025.111297","DOIUrl":"10.1016/j.ress.2025.111297","url":null,"abstract":"<div><div>Lithium-ion batteries are critical components of zero-emission electro-hydrogen generators (GEH2), where accurate performance prediction is essential for ensuring optimal operation and enabling effective predictive maintenance. Data-driven models have become increasingly prominent for predicting the State of Health (SOH) of lithium-ion batteries due to their high accuracy and reduced development time. However, in hybrid systems like GEH2, where the battery frequently remains inactive while the fuel cell supplies most of the power, the available battery data is limited. This data scarcity presents a significant challenge for achieving accurate SOH prediction. To address this challenge, we propose a novel data augmentation approach that integrates Time-series Generative Adversarial Network with a Transformer and a Gated Recurrent Unit to enhance data availability and improve prediction accuracy. This new approach enhances the model’s ability to capture long-term temporal dependencies within multivariate battery parameters while effectively addressing irregular time intervals, a common challenge in real-world batteries datasets. We evaluated the proposed approach using real-world industrial datasets from four distinct GEH2 batteries and two additional batteries from the publicly available NASA dataset. The performance of SOH prediction was assessed using a Long Short-Term Memory (LSTM) model trained on augmented data generated by various data augmentation techniques. The results consistently demonstrate that our approach outperforms all competing methods, highlighting its superior ability to enhance data for lithium-ion batteries. These findings highlight the effectiveness of our approach in enhancing predictive accuracy and robustness, making it highly suitable for real-world battery applications.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111297"},"PeriodicalIF":9.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298033","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":"Defending a series signaling system against uncertain attack time with individual protection, false nodes, and overarching protection","authors":"Bin Wang ,&nbsp;Gang Kou ,&nbsp;Hui Xiao","doi":"10.1016/j.ress.2025.111364","DOIUrl":"10.1016/j.ress.2025.111364","url":null,"abstract":"<div><div>This research addresses the resource allocation problem in protecting a series signaling system against intentional attacks considering uncertainties of attack time. We consider the integration of individual protection, deployment of false nodes, and overarching protection strategies to enhance system reliability. To model the strategic interaction between the attacker and defender, we formulate a two-stage min-max game model. Through numerical experiments, we analyze attack strategies aimed at maximizing the vulnerability of the entire system and discuss defense strategies focused on minimizing the total expected probability of destruction. Our findings reveal that the rate of resource stockpiling plays a pivotal role in determining system vulnerability, particularly under conditions of uncertain attack time. Furthermore, our study challenges the traditional intuition that a conservative centralized defense strategy is the most effective approach in such scenarios. These insights offer practical guidance for a system owner to improve system reliability when the attack time is uncertain.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111364"},"PeriodicalIF":9.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321901","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":"Critical transmission cut-sets identification within power grids","authors":"Lu Nan,&nbsp;Siduo Hu,&nbsp;Yuhang Chen,&nbsp;Chuan He","doi":"10.1016/j.ress.2025.111360","DOIUrl":"10.1016/j.ress.2025.111360","url":null,"abstract":"<div><div>It is crucial to quickly and accurately identify the critical transmission cut-sets to ensure the secure operation of power grids. This paper proposes a method for identifying the critical transmission cut-sets which form major flowgates from generators to loads within the power grid. Specially, the active power transaction between generator and load buses is proposed to derive the critical transmission cut-sets with the improved PageRank Algorithm. Firstly, the nodal criticality of buses is quantified on the basis of improved PageRank algorithm where the Google matrix and the relinking vector are modified. Furthermore, a new topology for identifying critical transmission cut-sets is constructed according to the nodal criticality of buses and the network transformation. The transmission cut-sets identification problem is thus converted into shortest paths searching in the new topology. The searching area is determined based on the active power transaction between generator and load buses. Finally, the set of transmission lines forming the critical transmission cut-sets, with highest criticalities between generator and load buses, are identified by searching the shortest paths. Numerical examples show that the proposed method can efficiently and accurately identify the critical transmission cut-sets within power grids, and provide guidance for system monitoring and protection.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111360"},"PeriodicalIF":9.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489380","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":"Time-variant reliability analysis using stratified Beta-sphere sampling and active learning","authors":"Yifan Cui ,&nbsp;Fangqi Hong ,&nbsp;Masaru Kitahara ,&nbsp;Pengfei Wei","doi":"10.1016/j.ress.2025.111295","DOIUrl":"10.1016/j.ress.2025.111295","url":null,"abstract":"<div><div>Estimating the failure probability of structures subjected to both time-invariant and time-variant stochastic inputs has long been reorganized as one of the most challenging tasks in structural engineering. Despite there are many developments for this problem, it still faces challenges in terms of accuracy and efficiency, especially for problems with small failure probability, highly nonlinearity and multiple disconnected failure domains that evolve over time. To fill this gap, a state-of-the-art stochastic simulation method utilizing stratified Beta-sphere sampling scheme is used to efficiently, accurately and robustly estimate the time-variant failure probability. Several novel developments, including a scheme to search the optimal training point, a single-layer strategy to train the Gaussian process regression (GPR) model, an adaptive filtering scheme to tackle the challenges caused by the potentially multiple failure domains, and remarkably, a new acquisition function for saving computational cost, have been presented in this work. The new acquisition function, called Time-variant Expected Integrated Error Reduction (TEIER) function, admits a prospective view as it measures the expected reward from refining the GPR model with a new point, and is capable of substantially reducing the required number of function calls. The superiority of the proposed methods in terms of efficiency, accuracy and robustness are demonstrated with numerical and engineering examples.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111295"},"PeriodicalIF":9.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280532","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":"Machine learning-enhanced fragility curves: Advancing reliability and safety of structures in seismic risk assessment","authors":"John Thedy ,&nbsp;Kuo-Wei Liao","doi":"10.1016/j.ress.2025.111361","DOIUrl":"10.1016/j.ress.2025.111361","url":null,"abstract":"<div><div>Fragility curves are essential in seismic risk assessment and performance-based design in structural engineering. The most accurate method to create these curves is through extensive Non-linear Time History Analysis (NLTHA) at various seismic intensities, assessing reliability across different PGAs. However, traditional fragility curves, constrained by computational costs, often oversimplified. This research introduces an innovative Autoregressive Neural Network (ARNN) for predicting structures’ time-history response during earthquakes, enabling more efficient fragility curve generation through cost-effective Monte Carlo Simulation (MCS). The ARNN’s unique input layer, which includes modal analysis to extract structural periods, windowed earthquake data, and structural responses, enables the handling of multiple structural parameters. Additionally, ARNN allows a single time history record to be partitioned into multiple training data sets, enhancing the efficiency of the machine learning. Differing from traditional fragility curves, this approach considers uncertainties in both ground motion and structural components, requiring 10–20 NLTHA records for ground motion alone and 125 to 300 records when considering both uncertainties. This methodology’s effectiveness is demonstrated through three numerical examples, including a nonlinear column, a damper-equipped structure, and a base-isolated building, significantly enhancing structural reliability and safety in seismic evaluations.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111361"},"PeriodicalIF":9.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280533","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":"An α-vector predictive value iteration algorithm for transportation infrastructure maintenance under partially observable conditions","authors":"Chunhui Guo,&nbsp;Zhenglin Liang","doi":"10.1016/j.ress.2025.111235","DOIUrl":"10.1016/j.ress.2025.111235","url":null,"abstract":"<div><div>Transportation infrastructure is degrading over time and poses the risk of failure when exposed to a dynamic environment. Periodic inspection is often implemented to assess the requirement of maintenance. However, the inspected conditions can only partially reflect the underlying degradation, complicating the decision of maintenance. Moreover, inspections of early degradation often have no value-adding to condition improvement and incur a portion of unnecessary expenses. To address the abovementioned issues, we propose a sequential predictive maintenance policy that accounts for the partial observation of the infrastructure’s condition to reduce unnecessary inspections. The schedule of inspection timings is predicted according to the estimated Remaining Useful Life distribution, adaptive to stochastic degradation. We demonstrate that the optimal value function is piecewise linear and convex when decision epochs are non-periodic. Leveraging this insight, we have designed an <span><math><mi>α</mi></math></span>-vector Predictive Value Iteration algorithm (<span><math><mi>α</mi></math></span>-PVI) to optimize the transportation infrastructure maintenance policy. The <span><math><mi>α</mi></math></span>-PVI algorithm further reduces the time complexity compared with the Point-Based Value Iteration algorithm. Our designed approach is verified through an application for the maintenance optimization of pavements and bridges. The results demonstrate that the <span><math><mi>α</mi></math></span>-PVI algorithm reduces unnecessary inspection costs by on average 61.25% when compared to the periodic inspection approach. The <span><math><mi>α</mi></math></span>-PVI algorithm constructs a new paradigm of predictive maintenance under partially observable conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111235"},"PeriodicalIF":9.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272518","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":"IoT-based mission reliability evaluation and maintenance optimization of intelligent manufacturing systems integrating human errors and heterogeneous feedstocks","authors":"Hongyan Dui ,&nbsp;Hengbo Wang ,&nbsp;Yong Yang ,&nbsp;Liudong Xing","doi":"10.1016/j.ress.2025.111354","DOIUrl":"10.1016/j.ress.2025.111354","url":null,"abstract":"<div><div>The rapid advancement of the Internet of Things (IoT) has driven significant interest in mission reliability evaluation and maintenance optimization for intelligent manufacturing systems (IMS) in intelligent manufacturing. However, existing studies have largely overlooked the impacts of human errors and heterogeneous feedstocks (qualified feedstocks and unqualified feedstocks) on machine degradation and buffer reliability. Additionally, the influence of maintenance priority constraints on the effectiveness of multi-objective optimization has received limited attention. Therefore, an IoT-based IMS mission reliability evaluation method is proposed, which incorporates the impacts of human errors and feedstocks. In addition, a multi-objective maintenance optimization algorithm that takes maintenance priority constraints into account is proposed. First, a new mission reliability modeling method considering heterogeneous feedstocks and human errors is proposed to characterize the impacts of interactions between processing machines, inspection machines, buffers, heterogeneous feedstocks, and humans on the degradation of manufacturing systems. Second, an IoT-based mission reliability evaluation method for manufacturing systems is proposed. Third, a multi-objective genetic algorithm (MOGA) with maintenance priority constraints is proposed to optimize reliability and cost. Finally, a case of an engine cylinder head manufacturing system is given to illustrate the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111354"},"PeriodicalIF":9.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339015","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":"Review of the concept of variability response function and its application in stochastic systems","authors":"George Deodatis ,&nbsp;Sanjay Arwade ,&nbsp;Lori Graham-Brady ,&nbsp;Kirubel Teferra","doi":"10.1016/j.ress.2025.111180","DOIUrl":"10.1016/j.ress.2025.111180","url":null,"abstract":"<div><div>Accurate stochastic analysis of structures is often complicated by the need for detailed probabilistic information about the random spatial variation of the underlying material/geometric properties. For many relevant properties, such as the flexibility or the elastic modulus, it is often possible to determine only their mean and standard deviation from the available measurement data. On the other hand, the majority of available stochastic structural models require knowledge of both the marginal probability distribution function and power spectrum (correlation function) of the stochastic field describing the uncertain system properties. The concept of Variability Response Function (VRF) emerged more than 35 years ago as an alternative to such a full stochastic analysis. The VRF can accomplish the following at a minimal computational cost: (i) establish realizable upper bounds on the random response variability based only on the mean and variance of the system properties, (ii) compute the response variance for a given power spectrum, (iii) perform a complete sensitivity analysis of the response variance with respect to the form of the power spectrum modeling the uncertain material/geometric properties, (iv) provide valuable insight into how different wavenumbers/wavelengths/scales of fluctuation contribute toward the overall value of the response variance. Since its initial inception for the response displacement of one-dimensional linear elastic structures, the VRF concept has been expanded to address displacements, internal forces, eigenvalues, and homogenized (effective) properties of structures in multiple dimensions, with multiple stochastic material properties, exhibiting nonlinear elastic constitutive behavior, and having large stochastic variations in their properties. Given the long timespan and the large body of work on VRFs, this paper provides a much-needed overview of all these previous developments that should prove useful to researchers seeking to develop VRF methods further or apply the approaches to practical engineering problems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111180"},"PeriodicalIF":9.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306290","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":"Dynamic predictive maintenance strategy for the multi-state system based on remaining life prediction","authors":"Junjie Zhu,&nbsp;Butong Li,&nbsp;Zhengbo Zhu,&nbsp;Xufeng Zhao","doi":"10.1016/j.ress.2025.111289","DOIUrl":"10.1016/j.ress.2025.111289","url":null,"abstract":"<div><div>In research on health management of complex systems, most predictive maintenance approaches focus on a single aspect of it, often lacking a holistic approach. To address this gap, we propose a comprehensive dynamic predictive maintenance strategy based on the remaining useful life (RUL) prediction method, designed to enable real-time system monitoring, dynamic forecasting, and optimized maintenance decision-making. Firstly, an integrated 1D-CNN-Informer prediction framework is introduced, which combines one-dimensional convolutional neural networks (1D-CNN) with the Informer model to predict RUL effectively. Secondly, based on the RUL predicted by the hybrid model, a dynamic predictive maintenance strategy for the system is developed. This strategy encompasses several key components, including spare parts ordering, inventory management, and maintenance decision-making, thereby forming a closed-loop maintenance decision system. For spare parts ordering, we further proposed a multi-state spare parts ordering strategy to optimize procurement decisions. This strategy dynamically determines the ordering status by evaluating the expected costs of inventory and out-of-stock, ensuring that overall costs are minimized while maintaining system reliability. Ultimately, the results of the experiment based on the turbofan engine dataset reveal that, compared to existing predictive maintenance strategies, the dynamic predictive maintenance framework we propose not only achieves more precise predictions but also demonstrates significant advantages in optimizing maintenance decisions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111289"},"PeriodicalIF":9.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272516","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}