Reliability Engineering & System Safety最新文献_第7页

Uncertainty quantification in predicting seismic response of high-speed railway simply-supported bridge system based on bootstrap

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-07 DOI: 10.1016/j.ress.2025.111006

Lingxu Wu , Wangbao Zhou , Tianxuan Zhong , Lizhong Jiang , Tianxing Wen , Lijun Xiong , Jiang Yi

{"title":"Uncertainty quantification in predicting seismic response of high-speed railway simply-supported bridge system based on bootstrap","authors":"Lingxu Wu , Wangbao Zhou , Tianxuan Zhong , Lizhong Jiang , Tianxing Wen , Lijun Xiong , Jiang Yi","doi":"10.1016/j.ress.2025.111006","DOIUrl":"10.1016/j.ress.2025.111006","url":null,"abstract":"<div><div>Reliable and rapid prediction of seismic-induced response is crucial for post-earthquake repair or rescue operations. In this paper, a method for quantifying uncertainty in rapid seismic response prediction for high-speed railway simply-supported bridge system (HRSBS) was developed based on a Bi-LSTM neural network surrogate model and Bootstrap resampling to address the challenge of acquiring timely seismic responses for HRSBS and the inability to determine confidence intervals from a single prediction result. Epistemic and aleatory uncertainties were quantified in rapid prediction of seismic-induced responses for HRSBS. The applicability of Bi-LSTM model based on a single seismic time series for predicting seismic-induced responses of HRSBS was identified. The results indicated that the prediction intervals with the 95% confidence level obtained by the proposed method encompass the actual values. The misjudgment rates of component damage states are effectively reduced. The Bi-LSTM model employing a single seismic time series input is suitable for predicting the time-history curves of seismic responses of components but not suitable for predicting seismic-induced residual displacement of rail.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111006"},"PeriodicalIF":9.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592801","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

FORM-based global reliability sensitivity analysis of systems with multiple failure modes

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-07 DOI: 10.1016/j.ress.2025.110974

Iason Papaioannou, Daniel Straub

引用次数: 0

Dynamic ensemble fault diagnosis framework with adaptive hierarchical sampling strategy for industrial imbalanced and overlapping data

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-06 DOI: 10.1016/j.ress.2025.110979

Haoyan Dong , Chuang Peng , Lei Chen , Kuangrong Hao

{"title":"Dynamic ensemble fault diagnosis framework with adaptive hierarchical sampling strategy for industrial imbalanced and overlapping data","authors":"Haoyan Dong , Chuang Peng , Lei Chen , Kuangrong Hao","doi":"10.1016/j.ress.2025.110979","DOIUrl":"10.1016/j.ress.2025.110979","url":null,"abstract":"<div><div>The coexistence of class imbalance and class overlap significantly challenges fault diagnosis in modern industrial processes. Class imbalance, characterized by the scarcity of fault data, and class overlap, arising from similarities between normal and fault data as well as correlations among fault types, are intertwined issues that jointly degrade fault diagnosis performance. To address these coupled issues, this paper proposes a dynamic ensemble fault diagnosis framework with adaptive hierarchical sampling strategy (DEAHS). The framework employs a boosting ensemble structure, effectively mitigating class imbalance through dynamic majority class undersampling and reducing class overlap by focusing on minority classes in high-overlap regions. In the outer layer, a Markov decision process guides the adaptive undersampling of majority class, achieving relatively balanced subsets. In the inner layer, a membership entropy-based method identifies overlap regions, and a weighted oversampling strategy improves minority classes’ representation in these regions. The proposed framework is validated on the Tennessee Eastman process and a real-world polyester esterification process, where its performance is evaluated using four metrics commonly employed for imbalanced datasets. The results demonstrate that the proposed method achieves superior performance across a majority of metrics, highlighting its effectiveness in handling imbalanced and overlapping industrial fault data.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110979"},"PeriodicalIF":9.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592799","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 sparrow search algorithm based co-correlation graph construction strategy for wind turbine group anomaly identification via graph attention networks

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-05 DOI: 10.1016/j.ress.2025.110998

Xiaomin Wang, Xiao Zhuang, Di Zhou, Jian Ge, Jiawei Xiang

{"title":"A novel sparrow search algorithm based co-correlation graph construction strategy for wind turbine group anomaly identification via graph attention networks","authors":"Xiaomin Wang, Xiao Zhuang, Di Zhou, Jian Ge, Jiawei Xiang","doi":"10.1016/j.ress.2025.110998","DOIUrl":"10.1016/j.ress.2025.110998","url":null,"abstract":"<div><div>Anomaly identification by using Supervisory Control and Data Acquisition (SCADA) data is an important means to improve the reliability of wind turbine group (WTG) operation. However, due to the low reliability of SCADA systems, anomalies in the data itself may occur as a result of sensor failures or data transmission errors. The anomalies in the data itself will reduce the accuracy and reliability of WTG anomaly identification. In this paper, a sparrow search algorithm (SSA) based co-correlation graph (CG) construction strategy using graph attention networks (SSACG-GAT) is proposed for WTG anomaly identification. First, the adjacency matrix representing the correlation of sample parameters is constructed by taking the monitoring parameters as nodes and the correlation between parameters as edges. Second, the proposed SSAGC strategy is used to obtain a co-correlation graph by fusing the adjacency matrices calculated by different correlation analysis models. In the proposed SSAGC strategy, the SSA is used to obtain the optimal fusion weights of the different adjacency matrices. Finally, the obtained optimal co-correlation graph is input into the GAT network for WTG anomaly identification. Nine models are selected as benchmarks to validate the effectiveness and superiority of the proposed SSACG-GAT. The experimental results show that the proposed SSACG-GAT has the best identification performance compared with nine benchmark methods. In addition, the ablation experiment results also demonstrate that the proposed SSACG strategy can effectively improve the accuracy and reliability of WTG anomaly identification.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110998"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578815","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

Early prediction of battery life using an interpretable health indicator with evolutionary computing

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-05 DOI: 10.1016/j.ress.2025.110980

Xueqi Xing, Tongtong Yan, Min Xia

{"title":"Early prediction of battery life using an interpretable health indicator with evolutionary computing","authors":"Xueqi Xing, Tongtong Yan, Min Xia","doi":"10.1016/j.ress.2025.110980","DOIUrl":"10.1016/j.ress.2025.110980","url":null,"abstract":"<div><div>Accurate prediction of battery lifespan is crucial for optimizing energy management, enhancing safety, and ensuring system reliability, particularly when only early-stage battery data is available. Health indicators (HIs) play a pivotal role in monitoring battery degradation by providing a link between the current state and the battery's end of life (EOL). However, existing methods for HI extraction often depend on extensive expert knowledge, large volumes of lifecycle data, and complex models to map HIs to battery lifespan. This study introduces an intelligent and interpretable methodology for generating HIs using improved genetic programming (GP) to enable rapid and precise battery lifespan prediction based solely on data from two early discharge cycles. Four HI candidates are derived from statistical features of the differences between discharge voltage curves. Unlike conventional methods that employ root mean square error (RMSE) as a fitness function, we introduce a novel correlation-based fitness function using cosine similarity within GP. This approach generates a transparent composite mathematical formula for extracting interpretable HIs. It automatically filters irrelevant HI candidates and combines relevant ones through specific mathematical operations. The resulting composite mathematical expression, universally applicable for constructing interpretable HIs across various cycle selections, enables rapid and early battery lifespan prediction through regression models. Validation on 124 battery cells shows that the proposed composite HI, expressed as an explicit mathematical function, achieves a mean absolute percentage error of approximately 15 % when predicting battery lifespan using data from just two cycles within the first 20 cycles across diverse operating conditions. Moreover, the proposed approach surpasses benchmark HIs in both prediction accuracy and stability across different regression models.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110980"},"PeriodicalIF":9.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547942","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

Agent-based fire evacuation model using social learning theory and intelligent optimization algorithms

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.111000

Peng Lu , Yufei Li

{"title":"Agent-based fire evacuation model using social learning theory and intelligent optimization algorithms","authors":"Peng Lu , Yufei Li","doi":"10.1016/j.ress.2025.111000","DOIUrl":"10.1016/j.ress.2025.111000","url":null,"abstract":"<div><div>Fire incidents often lead to a series of social problems. Therefore, it is particularly important to optimize evacuation strategies and promote relevant social safety knowledge. Based on this, the study proposes a fire evacuation model that integrates the Fire Dynamics Simulator (FDS) with Agent-Based Modeling (ABM) to simulate a bar fire scenario. In this model, the concept of social learning is introduced, and multiple factors such as evacuation time, trampling risk, and pedestrian health are considered as risk evaluation indicators. Machine learning combined with intelligent optimization methods is applied to optimize evacuation strategies. <strong>First</strong>, we validate the effectiveness of the model by comparing the averaged simulation results with real-world data. The results demonstrate that the simulation outcomes of our model exhibit good accuracy and robustness. <strong>Secondly</strong>, we analyze the importance of the second-floor safety exit. When the second-floor safety exit remains unobstructed, evacuation efficiency and casualty risk can be significantly improved. <strong>Then</strong>, we examine the role of social knowledge. When people are aware of the fire risk and choose to evacuate immediately, casualties can be significantly reduced. <strong>Finally</strong>, we study the effectiveness of phased evacuation in enhancing crowd safety. By employing a method that combines Random Forest and the Particle Swarm Optimization-Genetic Algorithm (PSO-GA), phased evacuation strategies are optimized, resulting in definitive strategies to reduce evacuation risks. This finding further expands social knowledge, indicating that when the proportion of staggered evacuation is appropriate, evacuation risks can be significantly reduced. Our research contributes to the development of social safety knowledge and provides methodological references for formulating evacuation strategies in different settings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111000"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563192","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

Coloured Petri Nets-based Approach for Modelling Effects of Variation on the Reliability of the Newborn Life Support Procedure

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.111001

Alfian Tan , Rasa Remenyte-Prescott , Joy Egede , Don Sharkey , Michel Valstar

{"title":"Coloured Petri Nets-based Approach for Modelling Effects of Variation on the Reliability of the Newborn Life Support Procedure","authors":"Alfian Tan , Rasa Remenyte-Prescott , Joy Egede , Don Sharkey , Michel Valstar","doi":"10.1016/j.ress.2025.111001","DOIUrl":"10.1016/j.ress.2025.111001","url":null,"abstract":"<div><div>About 10 % of newborns need a life support procedure following birth. However, this procedure has a considerable error rate of more than 25 %, which may compromise its safety and reliability. Continuous studies to improve its performance are carried out, but in-field studies can be expensive and not always feasible. Hence, a modelling approach is proposed. Studies have shown how variations and errors in this procedure are associated with technical and non-technical factors. Thus, the proposed approach includes these aspects by considering different settings of thermal care, the experience of the doctor, types of respiratory devices, and the ability of the clinical staff to cope with stress. The Coloured Petri Nets (CPNs) approach is used to model the characteristics of this Newborn Life Support (NLS) procedure. This technique can facilitate complex system modelling with a compact representation. The dynamic characteristics of the procedure are implemented during a simulation of the CPNs model. These relate to the duration of steps, the baby's physical response, and variations or errors from the required protocol. This paper demonstrates how risks in the protocol relating to the baby's final condition and clinical decision inaccuracies can be quantified by the approach.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111001"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592803","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}

引用次数: 0

Research on multi-factor adaptive integrated early warning method for coal mine disaster risks based on multi-task learning

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.111002

Chengfei Liu , Enyuan Wang , Zhonghui Li , Zesheng Zang , Baolin Li , Shan Yin , Chaolin Zhang , Yubing Liu , Jinxin Wang

{"title":"Research on multi-factor adaptive integrated early warning method for coal mine disaster risks based on multi-task learning","authors":"Chengfei Liu , Enyuan Wang , Zhonghui Li , Zesheng Zang , Baolin Li , Shan Yin , Chaolin Zhang , Yubing Liu , Jinxin Wang","doi":"10.1016/j.ress.2025.111002","DOIUrl":"10.1016/j.ress.2025.111002","url":null,"abstract":"<div><div>The reliable early warning of risks associated with gas, fire, dust, and roof hazards is crucial for the safe mining of coal mines. Traditional warning methods suffer from singular disaster risk warnings, low integration of risk information across different indicators, and insufficient perception of multi-hazard coupling relationships. To address these challenges, this paper proposes a method for adaptive integration of risk warnings that quantitatively learns the relationships between various indicators and warning tasks. Anomaly-transformer and E<sup>2</sup>GAN models are first employed to detect anomalies and impute missing values in time-series data. Subsequently, an improved MMoE model is used for multi-indicator fusion and prediction, allowing the simultaneous forecasting of future trends for all early-warning indicators. Finally, an adaptive multi-hazard risk integration warning model is developed, utilizing original and predicted data to calculate the current and future risk probabilities for various hazards. Comprehensive risk identification and warning are then performed using a multi-hazard grading identification. Experimental results show that the improved MMoE model outperforms LSTNet and TCN in prediction accuracy, and the integration model exceeds CNN and GRU in warning performance. Field validation confirms that this approach effectively identifies risks and enhances the reliability of intelligent early warning systems, ensuring coal mining safety.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111002"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592800","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 new multiple stochastic Kriging model for active learning surrogate-assisted reliability analysis

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.110966

Liangqi Wan , Yumeng Wei , Qiaoke Zhang , Lei Liu , Yuejian Chen

{"title":"A new multiple stochastic Kriging model for active learning surrogate-assisted reliability analysis","authors":"Liangqi Wan , Yumeng Wei , Qiaoke Zhang , Lei Liu , Yuejian Chen","doi":"10.1016/j.ress.2025.110966","DOIUrl":"10.1016/j.ress.2025.110966","url":null,"abstract":"<div><div>The Kriging model-assisted reliability analysis method is widely recognized as an effective way to evaluate structural failure probability. However, accurately estimating failure probability is challenging due to the inherent limitations of the Kriging model in accounting for response noise during the modeling process. This limitation undermines the accuracy of emulation in reliability analysis, significantly reducing the confidence of the reliability evaluation. To overcome this challenge, this paper proposes an active learning Lasso-based multiple stochastic Kriging model-Monte Carlo simulation method. First, a Voronoi-based adaptive proximity-guided sampling strategy is presented to sample important MCS points near the limit state surface by continuously identifying sensitive Voronoi cells. These identified MCS points are then used to select the stochastic Kriging model components, thereby ensuring that the selection process prioritizes the most informative regions. Second, a Lasso-based model selection strategy is proposed to account for the model-form uncertainty in the multiple stochastic Kriging modeling process, which optimizes and selects the best ensemble of multiple stochastic Kriging model components for the framework of the surrogate ensemble-assisted reliability analysis method. The effectiveness of the proposed method is demonstrated through numerical and engineering case studies. Results show that the proposed method provides more accurate failure probability estimation with fewer calls to limit state functions compared to existing methods, improving predictive accuracy and computational efficiency in structural reliability analysis.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110966"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563780","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 global attention based gated temporal convolutional network for machine remaining useful life prediction

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-03-04 DOI: 10.1016/j.ress.2025.110997

Xu Xinyao , Zhou Xiaolei , Fan Qiang , Yan Hao , Wang Fangxiao

{"title":"A global attention based gated temporal convolutional network for machine remaining useful life prediction","authors":"Xu Xinyao , Zhou Xiaolei , Fan Qiang , Yan Hao , Wang Fangxiao","doi":"10.1016/j.ress.2025.110997","DOIUrl":"10.1016/j.ress.2025.110997","url":null,"abstract":"<div><div>As the core technique of the prognostic and health management field, data-driven remaining useful life (RUL) prediction generally requires abundant data to construct reliable mappings from monitoring data to machines’ RUL labels. However, the diverse working conditions of machines can lead to their different degradation trajectories, which makes similar data indicate diverse RULs of different machines. When predicting RULs with monitoring data, the phenomenon causes a severe label confusion problem and limits the performance of data-driven RUL prediction methods. In this paper, a new gated-temporal-convolutional-network-based method is proposed for RUL prediction tasks of machines. To handle the label confusion problem, a novel global attention mechanism is proposed, which enables the proposed model to identify confused data by the difference in machines’ global degradation tendencies. Besides, a new temporal convolutional network with a gating mechanism is proposed for better feature extraction performance. Moreover, a new nearest-neighbor-based data compensation strategy is designed to simplify data distributions. Both strategies also contribute to the solution of the problem. The proposed method is verified on an aircraft turbofan engine dataset and a bearing dataset. The experiment results show the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110997"},"PeriodicalIF":9.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683421","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