Reliability Engineering & System Safety最新文献_第6页

Maximum cyclostationary characteristic energy index deconvolution and its application for bearing fault diagnosis 最大循环平稳特征能量指数反褶积及其在轴承故障诊断中的应用

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-08 DOI: 10.1016/j.ress.2025.111117

Xinyuan Zhao, Dongdong Liu, Lingli Cui

{"title":"Maximum cyclostationary characteristic energy index deconvolution and its application for bearing fault diagnosis","authors":"Xinyuan Zhao, Dongdong Liu, Lingli Cui","doi":"10.1016/j.ress.2025.111117","DOIUrl":"10.1016/j.ress.2025.111117","url":null,"abstract":"<div><div>Blind deconvolution can effectively highlight the fault impulses submerged in vibration signals. However, the objective function of existing deconvolution methods deeply depends on prior knowledge about fault periods, which is often difficult to acquire in advance or may lack accuracy in practical applications. Additionally, its filter length selection remains an open problem, hindering the performance and generalization in industrial scenarios. To address the above issues, a maximum cyclostationary characteristic energy index deconvolution (MCCEID) is proposed to recover periodic impulses, where a novel cyclostationary characteristic energy index (CCEI) is established as the objective function. The CCEI captures local variation features at the fault characteristic frequency (FCF) to iteratively enhance periodic components, instead of focusing on aperiodic noise. Meanwhile, a periodic hierarchical assessment method is developed to sequentially identify resonance frequency slice and FCF, in which the interference from other frequencies is excluded and only the resonance frequency slice is applied to estimate FCF. In addition, an adaptive filter length determination framework is designed considering both the value of CCEI and time cost, thereby avoiding the manual determination of the filter length. The performance of MCCEID is demonstrated by simulated and experimental signals, and the results illustrate that MCCEID outperforms the other methods in fault feature extraction.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111117"},"PeriodicalIF":9.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828793","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 soft actor-critic reinforcement learning-based method for remaining useful life prediction 基于软行为者-评论家强化学习的剩余使用寿命预测方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-08 DOI: 10.1016/j.ress.2025.111121

Shousheng Ding , Lei Meng , Jie Shang , Chen Jiang , Haobo Qiu , Liang Gao

{"title":"A soft actor-critic reinforcement learning-based method for remaining useful life prediction","authors":"Shousheng Ding , Lei Meng , Jie Shang , Chen Jiang , Haobo Qiu , Liang Gao","doi":"10.1016/j.ress.2025.111121","DOIUrl":"10.1016/j.ress.2025.111121","url":null,"abstract":"<div><div>Remaining useful life (RUL) prediction techniques play a crucial role in manufacturing equipment condition management and maintenance planning. Currently, data-driven deep learning methods have made significant advancements in this field. However, traditional approaches have not adequately considered the temporal correlations in both sensor data and RUL prediction values during the degradation process of equipment. The existing reinforcement learning (RL) methods face challenges such as lacking of sufficient lifespan variation information in the state variables, ignorance of dynamic changes in prediction error in the reward function design, and adoption of fixed interaction termination conditions that can't effectively promote the agent's learning of device degradation information. Therefore, this paper proposes a RL model based on the soft actor-critic (SAC) algorithm. Firstly, an autoencoder is employed to extract key features from the data collected by sensors. Subsequently, these key features, along with multi-dimensional lifespan features containing information from multiple historical time steps, are utilized to construct the state variables in RL. Next, a reward function is formulated taking into account error gradients. Finally, a progressive early stopping method is proposed to train the model. Extensive experiments are conducted on the CMAPSS dataset and XJTU-SY bearing dataset, and the proposed method demonstrates higher prediction accuracy compared to mainstream approaches.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111121"},"PeriodicalIF":9.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828796","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

An intelligent failure feature learning method for failure and maintenance data management of wind turbines 风电机组故障与维护数据管理的智能故障特征学习方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-08 DOI: 10.1016/j.ress.2025.111113

He Li , Yi Ding , Yu Sun , Min Xie , C. Guedes Soares

{"title":"An intelligent failure feature learning method for failure and maintenance data management of wind turbines","authors":"He Li , Yi Ding , Yu Sun , Min Xie , C. Guedes Soares","doi":"10.1016/j.ress.2025.111113","DOIUrl":"10.1016/j.ress.2025.111113","url":null,"abstract":"<div><div>This paper introduces an intelligent feature learning framework for the failure and maintenance data management of the wind energy sector. The framework employs Bidirectional Encoder Representations from Transformers and the Conditional Random Field model to intelligently identify failures in wind turbines. Additionally, a transfer training model is constructed to infer offshore wind turbine failures based on knowledge learned from onshore devices, which can address the insufficient knowledge of the offshore sector. The accuracy of the feature learning is enhanced by creating an adaptive resampling mechanism to detect features of rare failures often overlooked by high-frequency ones. Two failure and maintenance datasets, LGS-Onshore and LGS-Offshore, are collected and analysed to recognise differences in failure and maintenance between onshore and offshore wind turbines. The results demonstrate that this innovative data analysis framework outperforms existing methods, contributing to the wind energy sector's data foundation by providing essential datasets and new insights into wind farm operation and maintenance.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111113"},"PeriodicalIF":9.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844875","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

The bearing multi-sensor fault diagnosis method based on a multi-branch parallel perception network and feature fusion strategy 基于多分支并行感知网络和特征融合策略的轴承多传感器故障诊断方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-08 DOI: 10.1016/j.ress.2025.111122

Xueyi Li , Shuquan Xiao , Qi Li , Liangkuan Zhu , Tianyang Wang , Fulei Chu

{"title":"The bearing multi-sensor fault diagnosis method based on a multi-branch parallel perception network and feature fusion strategy","authors":"Xueyi Li , Shuquan Xiao , Qi Li , Liangkuan Zhu , Tianyang Wang , Fulei Chu","doi":"10.1016/j.ress.2025.111122","DOIUrl":"10.1016/j.ress.2025.111122","url":null,"abstract":"<div><div>Limited information from a single sensor constrains the precision of bearing fault diagnosis. Despite the abundance of multi-sensor data, the high dimensionality and complexity of data fusion make it difficult for existing methods to effectively extract and integrate multi-sensor features. To address these challenges, this paper proposes a novel multi-branch feature cross-fusion bearing fault diagnosis model (MCFormer), leveraging the powerful capabilities of Transformers in feature extraction and global modeling. First, to tackle the heterogeneity of multi-sensor data, a multi-branch structure is introduced to extract local features from each sensor separately, reducing information loss and redundancy. Then, based on the multi-branch feature extraction structure, a feature cross-fusion strategy and a dynamic classifier module are designed to achieve a unified representation of global features, enhancing feature discrimination and classification capabilities. Extensive experimental studies were conducted on two bearing cases, demonstrating that MCFormer achieves excellent diagnostic results on both the Northeast Forestry University (NEFU) bearing dataset and the Huazhong University of Science and Technology (HUST) bearing dataset, achieving diagnostic accuracies of 99.50 % and 98.33 %, respectively, surpassing the best performances of five other methods by 1.17 % and 2.36 %. Finally, ablation experiments confirm the efficacy of both component modules.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111122"},"PeriodicalIF":9.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824230","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

Optimizing expected working time in consecutive sliding window systems 连续滑动窗口系统的预期工作时间优化

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-08 DOI: 10.1016/j.ress.2025.111126

Narges Mahdavi-Nasab , Mostafa Abouei Ardakan , Enrico Zio

引用次数: 0

Enabling autonomous navigation: adaptive multi-source risk quantification in maritime transportation 实现自主导航：海上运输中的自适应多源风险量化

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-08 DOI: 10.1016/j.ress.2025.111118

Lichao Yang , Jingxian Liu , Qin Zhou , Zhao Liu , Yang Chen , Yukuan Wang , Yang Liu

{"title":"Enabling autonomous navigation: adaptive multi-source risk quantification in maritime transportation","authors":"Lichao Yang , Jingxian Liu , Qin Zhou , Zhao Liu , Yang Chen , Yukuan Wang , Yang Liu","doi":"10.1016/j.ress.2025.111118","DOIUrl":"10.1016/j.ress.2025.111118","url":null,"abstract":"<div><div>Current studies on maritime navigation risks often overlook interactions between ships, dynamic surroundings, and static environmental factors, limiting insights into navigation safety in complex scenarios. This research presents an innovative methodology to quantify and integrate multi-source heterogeneous navigation risks, enabling a comprehensive assessment of overall risk levels. The framework comprises four components. First, a spatiotemporal risk monitoring domain model, developed using historical AIS data, incorporates risk monitoring and forbidden domains, enabling precise localisation and timing of risk evaluation. Second, heterogeneous navigation risk evaluation functions, addressing dynamic target and static environment risks, capture ships’ varying sensitivities to diverse risk sources. Third, risk quantification methods evaluate dynamic risks from temporal and spatial perspectives while categorising static risks into three types. Finally, an adaptive fusion method hierarchically aggregates multi-source risk data into a unified profile, reflecting navigators’ risk perception. Real-world AIS data validate the framework, constructing spatiotemporal risk models for three ship types and analysing navigation scenarios such as crossing, overtaking, and multi-ship encounters. Results demonstrate the framework's capability to enhance precision in navigation risk assessment, providing actionable insights and robust support for autonomous navigation and intelligent maritime systems. This methodology offers a promising tool for advancing safety in complex maritime environments.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111118"},"PeriodicalIF":9.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825442","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

Damage risk assessment of transmission towers based on the combined probability spatial-temporal distribution of strong winds and earthquakes 基于强风与地震组合概率时空分布的输电塔破坏风险评估

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-06 DOI: 10.1016/j.ress.2025.111078

Xin Guo , Hongnan Li , Hao Zhang

{"title":"Damage risk assessment of transmission towers based on the combined probability spatial-temporal distribution of strong winds and earthquakes","authors":"Xin Guo , Hongnan Li , Hao Zhang","doi":"10.1016/j.ress.2025.111078","DOIUrl":"10.1016/j.ress.2025.111078","url":null,"abstract":"<div><div>Extreme loads can inflict significant damage on structures, posing serious risks to their safety and longevity. Numerous investigations have concentrated on the response of structures to isolated extreme loads, often neglecting the compounded damage resulting from multiple hazards, such as severe winds and seismic events. The lack of clear criteria for assessing the co-occurrence of strong winds and earthquakes, along with ambiguous spatial and temporal relationships and inadequate statistical methodologies, has resulted in a gap in comprehensive lifetime damage assessments based on the actual probabilities of these events occurring together. In this context, the present study introduces a data-driven methodology for damage risk assessment in engineering structures, taking into account the spatial and temporal probability distributions of strong winds and earthquakes. A joint probability distribution model was established utilizing 50 years of historical data from the Sichuan-Yunnan region of China, an area characterized by frequent occurrences of both strong winds and earthquakes, while integrating spatial-temporal criteria for the simultaneous occurrence of these phenomena. Utilizing this model, the nonlinear dynamic response characteristics of a representative transmission tower under the combined influence of strong winds and earthquakes were analyzed, alongside an evaluation of structural performance through fragility analysis and the damage risk probability associated with multi-hazard scenarios. The findings reveal that strong winds and earthquakes have co-occurred 996 times in the region over the past 50 years, underscoring the significant likelihood of simultaneous occurrences. Furthermore, in contrast to susceptibility-based structural performance assessments, the damage risk evaluation highlights the influence of high-probability disaster events on the lifecycle of the structure.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111078"},"PeriodicalIF":9.4,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816845","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

An active learning method combining MRBF model and dimension-reduction importance sampling for reliability analysis with high dimensionality and very small failure probability 将MRBF模型与降维重要性抽样相结合的主动学习方法用于高维小失效概率的可靠性分析

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-06 DOI: 10.1016/j.ress.2025.111107

Xufeng Yang, Wenke Jiang, Yu Zhang, Junyi Zhao

{"title":"An active learning method combining MRBF model and dimension-reduction importance sampling for reliability analysis with high dimensionality and very small failure probability","authors":"Xufeng Yang, Wenke Jiang, Yu Zhang, Junyi Zhao","doi":"10.1016/j.ress.2025.111107","DOIUrl":"10.1016/j.ress.2025.111107","url":null,"abstract":"<div><div>Multiple surrogate models suffer from the curse of dimensionality and Radial basis function (RBF) model is particularly well-suited for approximating of high-dimensional performance functions. Additionally, by leveraging matrix operations, the prediction time of RBF model can be significantly reduced. However, when the failure probability becomes extremely small, the prediction time of matrix-operation RBF (MRBF) model is also prohibitive. To address the challenges posed by both high dimensionality and very small failure probability, we propose an active learning method that fuses the MRBF model with a novel importance sampling method—iCE-m*. iCE-m* is a cross-entropy importance sampling embedded dimensionality reduction mechanism. Firstly, we define the instrumental density series of iCE-m* based on the prediction information of MRBF, which fuels iCE-m* to generate candidate samples covering the region near the limit state surface. Then, we propose a new learning function that measures the coefficient of variation of the square of the performance function, which helps identify the optimal training points near the limit state surface. The performance of the proposed method is demonstrated through five high-dimensional problems. Compared with state-of-the-art methods, the proposed method is highly competitive in terms of both function evaluations and computation time.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111107"},"PeriodicalIF":9.4,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824089","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

Application of physics-informed machine learning in performance degradation and RUL prediction of hydraulic piston pumps 物理信息机器学习在液压柱塞泵性能退化和有效使用时间预测中的应用

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-05 DOI: 10.1016/j.ress.2025.111108

Yadong Zhang , Shaoping Wang , Chao Zhang , Hongyan Dui , Rentong Chen

{"title":"Application of physics-informed machine learning in performance degradation and RUL prediction of hydraulic piston pumps","authors":"Yadong Zhang , Shaoping Wang , Chao Zhang , Hongyan Dui , Rentong Chen","doi":"10.1016/j.ress.2025.111108","DOIUrl":"10.1016/j.ress.2025.111108","url":null,"abstract":"<div><div>Hydraulic pumps have been widely used in various application domains, especially in aerospace and industrial machinery. Therefore, the accurate prediction of the performance degradation and remaining useful life (RUL) is crucial for ensuring the high reliability and safety of hydraulic pump pressure supply systems. However, the hydraulic pump performance degradation is a complex multi-factor coupling process, which is affected by the wear of internal key friction pairs and external operating conditions. This paper proposes a method that integrates failure mechanisms with data-driven approaches to forecast the degradation trajectory of hydraulic pumps, considering both the wear and operating conditions. Based on the friction and wear failure mechanism, wear evaluation models for three key friction pairs are first developed, and the wear of the hydraulic pump is evaluated by the output of the model. A Long Short-Term Memory (LSTM) network is then used to construct the mapping relationship between the wear level and return oil flow of the hydraulic pump. Afterwards, the dynamic model is iterated by updating the degradation sensitive parameters of three pairs of key friction pairs. Finally, the effectiveness of the proposed physics-informed LSTM (PI-LSTM) network framework is verified on four collected hydraulic pump degradation datasets, and compared with those of state-of-the-art methods.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111108"},"PeriodicalIF":9.4,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816843","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

Leveraging digital twin for healthcare emergency management system: Recent advances, critical challenges, and future directions 将数字孪生应用于医疗急救管理系统：最新进展、关键挑战和未来方向

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-04-05 DOI: 10.1016/j.ress.2025.111079

Ruiyan Zheng , S. Thomas Ng , Yuyang Shao , Zhongfu Li , Jiduo Xing

{"title":"Leveraging digital twin for healthcare emergency management system: Recent advances, critical challenges, and future directions","authors":"Ruiyan Zheng , S. Thomas Ng , Yuyang Shao , Zhongfu Li , Jiduo Xing","doi":"10.1016/j.ress.2025.111079","DOIUrl":"10.1016/j.ress.2025.111079","url":null,"abstract":"<div><div>In the post COVID-19 era, there is an escalating demand to fundamentally rethink and digitalize healthcare emergency management (HEM) to ensure greater resilience and responsiveness. Among emerging technologies, the digital twin (DT) holds unique promise by enabling real-time monitoring, dynamic decision support, and predictive maintenance, all of which are critical in high-stakes emergency scenarios. Despite its potential, DT deployment in HEM remains an intricate, long-term endeavor, hampered by significant conceptual and technical barriers. Many stakeholders lack a clear understanding of DT's functional scope, the requisite technologies for robust implementation, and pathways for integrating DT into established healthcare workflows. In response, this paper offers a comprehensive examination of DT in HEM, categorizing current applications across four levels: individual, hospital, public, and cloud supporting. This paper also highlights how contemporary technical solutions, ranging from advanced networking and distributed computing to AI-driven analytics, can be orchestrated to support novel DT functionalities in real-world healthcare operations. Additionally, challenges, open problems and future directions for DT in HEM are discussed. By synthesizing both functional and research-oriented insights, this review aims to clarify future directions for leveraging DT as a transformative vehicle for healthcare emergency preparedness, response, and long-term resilience.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111079"},"PeriodicalIF":9.4,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824233","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