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

An efficient sequential Kriging model for structure safety lifetime analysis considering uncertain degradation

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-20 DOI: 10.1016/j.ress.2024.110669

Peng Hao, Haojun Tian, Hao Yang, Yue Zhang, Shaojun Feng

{"title":"An efficient sequential Kriging model for structure safety lifetime analysis considering uncertain degradation","authors":"Peng Hao, Haojun Tian, Hao Yang, Yue Zhang, Shaojun Feng","doi":"10.1016/j.ress.2024.110669","DOIUrl":"10.1016/j.ress.2024.110669","url":null,"abstract":"<div><div>Safety lifetime analysis performs a crucial role in ensuring structural safety in service and developing effective maintenance strategies, which also places higher demands on calculation. However, existing safety lifetime analysis methods generally suffer from inefficiency, which is more prominent for complex engineering structures. In this paper, a novel sequential single-loop Kriging (SSK) surrogate modeling approach is proposed to calculate the safety lifetime in an efficient and accurate manner. To reduce the computational cost, a single-loop safety lifetime analysis framework is proposed. In this framework, there is no need to accurately calculate the time-dependent failure probability (TDFP) in any sub-time interval. By searching the safety lifetime in the process of time-dependent reliability analysis (TRA) and dynamically adjusting the interest time interval, the safety lifetime can be quickly determined by constructing only one Kriging model. To maximize the utilization of sample information, SSK employs a modified learning function that allows most of the training points to be added before the safety lifetime. For accuracy, a convergence criterion that includes two Kriging models is proposed. Mathematical engineering examples are used to illustrate the accuracy and efficiency of SSK. The proposed method offers a promising approach for efficient safety lifetime analysis of engineering problems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110669"},"PeriodicalIF":9.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744667","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

Inspection and maintenance of a system with a bypass component

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-20 DOI: 10.1016/j.ress.2024.110649

M.D. Berrade , E. Calvo , F.G. Badía

引用次数: 0

Bayesian structural reliability updating using a population track record

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-20 DOI: 10.1016/j.ress.2024.110644

R. de Vries , R.D.J.M. Steenbergen , A.C.W.M. Vrouwenvelder

{"title":"Bayesian structural reliability updating using a population track record","authors":"R. de Vries , R.D.J.M. Steenbergen , A.C.W.M. Vrouwenvelder","doi":"10.1016/j.ress.2024.110644","DOIUrl":"10.1016/j.ress.2024.110644","url":null,"abstract":"<div><div>In the assessment of existing structures, it is uncommon to consider a track record of the structural performance of the structure itself or similar structures. However, the structure's proven strength in service could play a significant role, along with the performance of similar structures in the population. Because the population track record does not apply in the design of new structures, it is not encountered in design standards. An assessment that does not incorporate the track record may conclude insufficient structural reliability whilst, in reality, the reliability is satisfactory. In the suggested approach, information obtained from laboratory experiments is combined with the track record in a Bayesian way to assess a structure's reliability. As a case study for this article, the reliability of the connection strength between wide slab floor elements is considered. Although laboratory tests indicate poor connection strength, the track record indicates just one failure and many well-performing floors. It is found that considering the time-dependent nature of structural reliability is vital for understanding how proven strength develops from the completion of the structure to its usage today. The number of similar objects in the population that show satisfactory performance is varied and is shown to have a significant effect when its number grows. The presented method and case study show that reliability assessments incorporating a track record enable more accurate structural reliability predictions for existing structures.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110644"},"PeriodicalIF":9.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744670","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 fault hierarchical propagation reliability improvement method for CNC machine tools based on spatiotemporal factors coupling

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-19 DOI: 10.1016/j.ress.2024.110672

Congbin Yang , Yongqi Wang , Jun Yan , Zhifeng Liu , Tao Zhang

{"title":"A fault hierarchical propagation reliability improvement method for CNC machine tools based on spatiotemporal factors coupling","authors":"Congbin Yang , Yongqi Wang , Jun Yan , Zhifeng Liu , Tao Zhang","doi":"10.1016/j.ress.2024.110672","DOIUrl":"10.1016/j.ress.2024.110672","url":null,"abstract":"<div><div>Clarifying the fault propagation mechanism is one of the key methods for improving the machine tool's reliability. However, current modeling methods usually overlook the impact of spatiotemporal coupling factors on fault propagation, leading to a limited understanding of the fault propagation mechanism. Therefore, this paper proposes a fault hierarchical propagation reliability improvement method based on spatiotemporal factors coupling. Considering the coupling effects of component comprehensive importance, fault tolerance, and failure modes on the machine tool system, a spatiotemporal fault hierarchical propagation topological directed graph model was established. Based on this, an improved method for calculating fault propagation strength was proposed to identify weak links and critical fault propagation paths. The proposed method effectively addresses the critical path identification problem across CNC machine tool systems. Comparison results demonstrate that the proposed method can accurately identify critical fault propagation paths. Furthermore, the influence of various factors on these path sequences is studied in this paper. It extends the traditional modeling methods and theories to enhance the transparency of the fault propagation process within the machine tool system. This work provides theoretical support for maintenance decision-making.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110672"},"PeriodicalIF":9.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744668","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

Mixed style network based: A novel rotating machinery fault diagnosis method through batch spectral penalization

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-19 DOI: 10.1016/j.ress.2024.110667

Xueyi Li , Tianyu Yu , Feibin Zhang , Jinfeng Huang , David He , Fulei Chu

{"title":"Mixed style network based: A novel rotating machinery fault diagnosis method through batch spectral penalization","authors":"Xueyi Li , Tianyu Yu , Feibin Zhang , Jinfeng Huang , David He , Fulei Chu","doi":"10.1016/j.ress.2024.110667","DOIUrl":"10.1016/j.ress.2024.110667","url":null,"abstract":"<div><div>The unsupervised fault diagnosis of rotating machinery holds significant importance, but it still faces numerous complex challenges. For instance, traditional convolutional neural networks often overlook inter-channel relationships, resulting in poor generalization and requiring manual adjustment of architecture parameters for different tasks. Additionally, traditional domain adversarial transfer learning has insufficient research on feature discriminability, leading to less distinguishable features. To address these issues, this paper proposes a MixStyle network based on the SE attention mechanism. This method achieves dynamic weight allocation through the SE attention mechanism, which is simple in design and introduces few additional parameters. By employing the MixStyle method for probabilistic mixed-domain training, the diversity of the source domain is increased, thereby improving the model's generalization capability. Since the principal singular vector enhances feature transferability, this paper penalizes the largest singular value through Batch Spectral Penalization to enhance other feature vectors, improving feature discriminability and domain adversarial performance. Experimental results show that the proposed method demonstrates outstanding performance in the task of unsupervised fault diagnosis for rotating machinery.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110667"},"PeriodicalIF":9.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744672","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

Probabilistic modelling of steel column response to far-field detonations

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-19 DOI: 10.1016/j.ress.2024.110665

Jaswanth Gangolu, Hezi Y. Grisaro

{"title":"Probabilistic modelling of steel column response to far-field detonations","authors":"Jaswanth Gangolu, Hezi Y. Grisaro","doi":"10.1016/j.ress.2024.110665","DOIUrl":"10.1016/j.ress.2024.110665","url":null,"abstract":"<div><div>Due to the deficiency of current design guidelines for blast loadings on steel structures, this research develops probabilistic models for steel wide-flange columns under axial and far-field blast loading on both their weak and strong axes. A total of 160 finite element (FE) simulations were conducted using ANSYS LS-DYNA, with columns subjected to different Axial Load Ratios (ALRs) and blast impulses. Validation against two experimental tests showed a strong correlation in displacement plots, with a material model accounting for strain rate effects. Probabilistic models for predicting maximum displacement and residual axial capacity were formulated using Bayesian inference and posterior statistics. These models were developed by incorporating dimensionless physics-based explanatory functions. The slenderness ratio of the column was identified as the most influential. The models account for uncertainties such as material and geometric properties, as well as strain rate effects. Graphical plots between the ALR and Damage Index (DI) were examined to assess the column's damage level. Furthermore, the probability of failure (fragility) of four columns for similar blast impulse was assessed w.r.t DI. These models along with ALR vs DI plots will be useful tools to know the level of building occupancy and retrofitting options.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110665"},"PeriodicalIF":9.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability model and maintenance cost optimization of wind-photovoltaic hybrid power systems

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-19 DOI: 10.1016/j.ress.2024.110673

Chao Zhang , Qi Zeng , Hongyan Dui , Rentong Chen , Shaoping Wang

{"title":"Reliability model and maintenance cost optimization of wind-photovoltaic hybrid power systems","authors":"Chao Zhang , Qi Zeng , Hongyan Dui , Rentong Chen , Shaoping Wang","doi":"10.1016/j.ress.2024.110673","DOIUrl":"10.1016/j.ress.2024.110673","url":null,"abstract":"<div><div>Power systems are becoming the backbone for replacing fossil energy sources in powering human life, including wind, solar, hydropower, and nuclear energy. However, a power system is intermittent, while the integration of multiple systems allows to reduce the impact of intermittency and to increase the reliability. This paper studies the wind-photovoltaic hybrid power system and its complementary strategy and maintenance cost under different failure modes and scenarios. A reliability model of the wind-photovoltaic power system is developed based on the critical wind turbine components and the topological structure of photovoltaic (PV) systems. A maintenance cost model is then derived while considering the corrective maintenance and preventive maintenance. Afterward, a maintenance optimization model is developed while incorporating some strategies of energy complementarity. Finally, a case study in Zhejiang Province, China is adopted to verify the efficiency of the proposed method, the minimum number for proper work of the PV power subsystem, and the energy complementarity between wind and PV power system.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110673"},"PeriodicalIF":9.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744550","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

Improved multiple penalty mechanism based loss function for more realistic aeroengine RUL advanced prediction

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-19 DOI: 10.1016/j.ress.2024.110666

Chaojing Lin , Yunxiao Chen , Mingliang Bai , Zhenhua Long , Peng Yao , Jinfu Liu , Daren Yu

{"title":"Improved multiple penalty mechanism based loss function for more realistic aeroengine RUL advanced prediction","authors":"Chaojing Lin , Yunxiao Chen , Mingliang Bai , Zhenhua Long , Peng Yao , Jinfu Liu , Daren Yu","doi":"10.1016/j.ress.2024.110666","DOIUrl":"10.1016/j.ress.2024.110666","url":null,"abstract":"<div><div>The aeroengine remaining useful life (RUL) prediction is conducive to formulating maintenance plans, assisting maintenance decisions, and improving the intelligent operation and maintenance level. When the engine is in a degraded state, the maintenance personnel tend to prediction advance rather than prediction delay. However, the current RUL prediction researches mainly focus on accurate prediction, and pay little attention to the realistic demand of advanced prediction. Aiming at this problem, this paper proposes a multiple penalty mechanism (MPM) based loss function combined with similarity RUL prediction. This research first uses multi-dimensional sensor data to construct a health index (HI) that characterizes the engine health status, then matches the HI similarity by derivative dynamic time warping corrected with different sequence length (DDTW-DSL). Finally, the MPM loss function assists the neural network to realize the mapping from HI to RUL. The method is verified by NASA's Commercial Modular Aero-Propulsion System Simulation dataset. The results show that compared with the traditional RMSE loss function, the MPM loss function can significantly improve the advanced prediction probability and effectively avoid RUL prediction lag. Compared with the existing methods, the novel method has advantages in both RUL prediction effect and model complexity.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110666"},"PeriodicalIF":9.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744560","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

Diagnostics and Prognostics in Power Plants: A systematic review

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-18 DOI: 10.1016/j.ress.2024.110663

Wei Cheng , Hassaan Ahmad , Lin Gao , Ji Xing , Zelin Nie , Xuefeng Chen , Zhao Xu , Rongyong Zhang

{"title":"Diagnostics and Prognostics in Power Plants: A systematic review","authors":"Wei Cheng , Hassaan Ahmad , Lin Gao , Ji Xing , Zelin Nie , Xuefeng Chen , Zhao Xu , Rongyong Zhang","doi":"10.1016/j.ress.2024.110663","DOIUrl":"10.1016/j.ress.2024.110663","url":null,"abstract":"<div><div>Failures in power plants can lead to significant power interruptions and economic losses. Prognostics and Health Management (PHM) serves as a predictive maintenance technique by detecting and diagnosing faults while forecasting potential failures. This systematic review analyzes trends in diagnosis and prognosis in power plants using scientometric analysis, summarizes the datasets and components targeted by researchers, outlines the advantages and drawbacks of popular methods, and reports detailed methodologies from selected literature. The complex nature of power plants presents significant challenges for implementing PHM effectively. Data-driven techniques, particularly machine learning and deep learning, have emerged as popular solutions to address these challenges. While diagnostic methods have seen substantial advancements, prognostics in power plants remain underdeveloped and require further investigation. This paper discusses the challenges associated with fault diagnosis and prognosis, emphasizing that addressing these issues could significantly enhance the effectiveness of PHM. By reviewing recent methodological advancements, summarizing the pros and cons of various methods, and identifying key challenges, this paper contributes to a deeper understanding of the field and highlights opportunities for future research.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110663"},"PeriodicalIF":9.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744678","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 integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-18 DOI: 10.1016/j.ress.2024.110664

Lin Wang , Wannian Guo , Junyu Guo , Shaocong Zheng , Zhiyuan Wang , Hooi Siang Kang , He Li

{"title":"An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features","authors":"Lin Wang , Wannian Guo , Junyu Guo , Shaocong Zheng , Zhiyuan Wang , Hooi Siang Kang , He Li","doi":"10.1016/j.ress.2024.110664","DOIUrl":"10.1016/j.ress.2024.110664","url":null,"abstract":"<div><div>Pipeline feature recognition is crucial for the reliability and safety of long-distance natural gas pipelines. Utilizing manual or machine learning methods to recognize pipeline features is not only inefficient, but also has problems such as high misjudgment rate and poor robustness. To overcome the above problems, this paper proposes a pipeline feature recognition method based on Multi-scale Attention Convolutional Neural Network (MACNN) and Gated_Twins_Transformer. MACNN is used to extract multi-scale information of pipeline features, and then the attention mechanism in it to focus on the more important feature information and suppress the less important feature information. It is then transmitted to the Gated_Twins_Transformer model, which uses the gated mechanism and the twins encoder module to determine the importance of the data length and input dimensions, focusing on the feature information of both with different weights, and the Transformer enhances the extraction of global features. Finally, the measured pipeline bending strain data are used as model input, trained and tested, and compared with other advanced models, the superiority of the proposed model in this paper is verified by comparing the metrics of Accuracy, Precision, Recall and F1-score.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110664"},"PeriodicalIF":9.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744548","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