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

An efficient sequential Kriging model for structure safety lifetime analysis considering uncertain degradation 考虑不确定退化的结构安全寿命分析的有效序贯Kriging模型

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

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

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.

安全寿命分析对于保证结构的安全使用和制定有效的维修策略至关重要，同时也对计算提出了更高的要求。然而，现有的安全寿命分析方法普遍存在效率低下的问题，这在复杂的工程结构中表现得更为突出。为了高效、准确地计算安全寿命，提出了一种新的顺序单回路Kriging （SSK）代理建模方法。为了减少计算成本，提出了一种单回路安全寿命分析框架。在该框架中，不需要在任意子时间区间内精确计算随时间变化的失效概率（TDFP）。通过在时变可靠性分析（TRA）过程中搜索安全寿命并动态调整兴趣时间间隔，只需构建一个Kriging模型即可快速确定安全寿命。为了最大限度地利用样本信息，SSK采用了一种改进的学习函数，允许在安全寿命之前添加大部分训练点。为了提高精度，提出了一个包含两个Kriging模型的收敛准则。数学工程实例说明了SSK的准确性和效率。该方法为工程问题的安全寿命分析提供了一种有效的方法。

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引用次数: 0

Inspection and maintenance of a system with a bypass component 带旁路组件的系统的检查和维护

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-11-20 DOI: 10.1016/j.ress.2024.110649

M.D. Berrade , E. Calvo , F.G. Badía

We present an inspection and maintenance model for a two-component lubrication system, filter and bypass valve, with applications to centralized lubrication systems. It presents significant differences from redundant systems in previous studies on cold, warm or hot stand-by components. These are the dissimilarity between the filter and the bypass, as the latter can induce catastrophic damage after a long working period, and the stochastic dependence between the filter and the bypass valve. Inspection and testing is focused on the valve, and only if it fails to open on inspection, or it is found to be open, is the filter inspection triggered. Preventive maintenance is mainly concerned with the filter, which is replaced periodically and also when an inspection detects an open valve or a clogged filter. The sensitivity analysis reveals that the optimum policy depends more on the parameters defining the lifetime of the filter than on those of the valve.

提出了一种双组分润滑系统、过滤器和旁通阀的检测与维护模型，并应用于集中润滑系统。它与以往研究中关于冷、热或热备用部件的冗余系统存在显著差异。这是滤波器与旁通的不同之处，因为后者在长时间工作后会引起灾难性的损坏，以及滤波器与旁通阀之间的随机依赖性。检查和测试的重点是阀门，只有在检查时阀门没有打开，或者发现阀门是打开的，才会触发过滤器检查。预防性维护主要涉及过滤器，定期更换，也可在检查发现阀门打开或过滤器堵塞时更换。灵敏度分析表明，最优策略更多地取决于定义过滤器寿命的参数，而不是阀门的参数。

引用次数: 0

Bayesian structural reliability updating using a population track record 基于种群跟踪记录的贝叶斯结构可靠性更新

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

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

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.

在评估现有结构时，很少考虑结构本身或类似结构的结构性能记录。然而，这种结构在服役中被证明的强度可能会发挥重要作用，同时在人群中也有类似结构的表现。由于人口跟踪记录不适用于新结构的设计，因此在设计标准中没有遇到。不包含跟踪记录的评估可能得出结构可靠性不足的结论，而实际上，可靠性是令人满意的。在建议的方法中，从实验室实验中获得的信息以贝叶斯方法与跟踪记录相结合，以评估结构的可靠性。作为实例，本文考虑了宽板楼盖单元间连接强度的可靠性。虽然实验室测试表明连接强度较差，但记录显示只有一次失败，许多楼层表现良好。研究发现，考虑结构可靠性的时变特性对于理解从结构完工到今天使用的验证强度是如何发展的至关重要。在总体中表现出令人满意性能的相似对象的数量是不同的，并且当其数量增加时显示出显著的影响。所提出的方法和案例研究表明，结合跟踪记录的可靠性评估可以对现有结构进行更准确的结构可靠性预测。

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引用次数: 0

A fault hierarchical propagation reliability improvement method for CNC machine tools based on spatiotemporal factors coupling 基于时空因素耦合的数控机床故障分层传播可靠性改进方法

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

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

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.

弄清故障传播机理是提高机床可靠性的关键方法之一。然而，目前的建模方法往往忽略了时空耦合因素对故障传播的影响，导致对故障传播机制的理解有限。为此，本文提出了一种基于时空因素耦合的故障分层传播可靠性改进方法。考虑部件综合重要性、容错能力和故障模式对机床系统的耦合效应，建立了机床系统故障的时空层次传播拓扑有向图模型。在此基础上，提出了一种改进的故障传播强度计算方法，用于识别薄弱环节和关键故障传播路径。该方法有效地解决了数控机床系统关键路径辨识问题。对比结果表明，该方法能够准确识别关键故障的传播路径。此外，本文还研究了各种因素对路径序列的影响。它扩展了传统的建模方法和理论，提高了机床系统故障传播过程的透明度。该工作为维修决策提供了理论支持。

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引用次数: 0

Mixed style network based: A novel rotating machinery fault diagnosis method through batch spectral penalization 基于混合网络的旋转机械故障诊断方法

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

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

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.

旋转机械的无监督故障诊断具有重要意义，但仍面临许多复杂的挑战。例如，传统的卷积神经网络往往忽略通道间的关系，导致泛化能力差，需要人为调整不同任务的架构参数。此外，传统的领域对抗迁移学习对特征可判别性的研究不足，导致特征可区分性较差。为了解决这些问题，本文提出了一个基于SE注意机制的MixStyle网络。该方法通过SE关注机制实现权重动态分配，设计简单，引入的附加参数少。采用MixStyle方法进行概率混合域训练，增加了源域的多样性，从而提高了模型的泛化能力。由于主奇异向量增强了特征的可转移性，本文通过批处理谱惩罚对最大奇异值进行惩罚来增强其他特征向量，从而提高特征的可辨别性和域对抗性能。实验结果表明，该方法在旋转机械的无监督故障诊断任务中表现出优异的性能。

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引用次数: 0

Probabilistic modelling of steel column response to far-field detonations 钢柱对远场爆炸响应的概率模拟

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Reliability Engineering & System Safety

Pub Date : 2024-11-19 DOI: 10.1016/j.ress.2024.110665

Jaswanth Gangolu, Hezi Y. Grisaro

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.

针对目前钢结构爆炸荷载设计指南的不足，本文建立了钢宽法兰柱在弱轴和强轴上承受轴向和远场爆炸荷载的概率模型。采用ANSYS LS-DYNA软件对柱体进行了160次有限元模拟，柱体受到不同轴向载荷比（alr）和冲击波的影响。对两个实验测试的验证表明，位移图与考虑应变率效应的材料模型之间存在很强的相关性。利用贝叶斯推理和后验统计建立了预测最大位移和剩余轴向容量的概率模型。这些模型是通过结合无量纲物理的解释函数而发展起来的。柱的长细比是影响最大的因素。这些模型考虑了材料和几何特性以及应变率效应等不确定性。检查损伤指数（DI）与ALR之间的图形图，以评估柱的损伤程度。在此基础上，对四根柱在相似爆炸冲击下的破坏概率（易碎性）进行了评价。这些模型以及ALR和DI图将是了解建筑物占用水平和改造选择的有用工具。

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引用次数: 0

Reliability model and maintenance cost optimization of wind-photovoltaic hybrid power systems 风电-光伏混合动力系统可靠性模型及维护成本优化

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

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

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.

电力系统正在成为替代化石能源为人类生活提供动力的支柱，包括风能、太阳能、水电和核能。然而，一个电力系统是间歇性的，而多个系统的集成可以减少间歇性的影响，提高可靠性。本文研究了不同故障模式和场景下的风光伏混合发电系统及其互补策略和维护成本。基于风电机组关键部件和光伏系统拓扑结构，建立了风电光伏发电系统可靠性模型。在此基础上，建立了考虑纠正性维修和预防性维修的维修成本模型。在此基础上，结合能源互补策略，建立了维修优化模型。最后，以中国浙江省为例，验证了所提方法的有效性、光伏发电分系统正常工作的最小数量以及风电与光伏发电系统的能量互补性。

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引用次数: 0

Improved multiple penalty mechanism based loss function for more realistic aeroengine RUL advanced prediction 改进的基于多重惩罚机制的损失函数，用于更真实的航空发动机RUL预估

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

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

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.

航空发动机剩余使用寿命（RUL）预测有利于制定维修计划，辅助维修决策，提高智能运维水平。当发动机处于退化状态时，维修人员倾向于预测提前而不是预测延迟。然而，目前的RUL预测研究主要集中在准确预测上，很少关注超前预测的现实需求。针对这一问题，本文提出了一种基于多重惩罚机制（MPM）的损失函数，并结合相似RUL预测。本研究首先利用多维传感器数据构建表征发动机健康状态的健康指数（HI），然后通过不同序列长度修正的导数动态时间规整（DDTW-DSL）对HI相似度进行匹配。最后，MPM损失函数帮助神经网络实现从HI到RUL的映射。该方法通过NASA的商用模块化航空推进系统仿真数据集进行了验证。结果表明，与传统的RMSE损失函数相比，MPM损失函数能显著提高超前预测概率，有效避免RUL预测滞后。与现有方法相比，该方法在RUL预测效果和模型复杂度方面都具有优势。

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引用次数: 0

Diagnostics and Prognostics in Power Plants: A systematic review 电厂诊断与预测：系统回顾

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

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

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.

发电厂的故障会导致严重的电力中断和经济损失。预测和健康管理（PHM）作为一种预测性维护技术，在预测潜在故障的同时检测和诊断故障。本系统综述分析了利用科学计量学分析在电厂诊断和预后方面的趋势，总结了研究人员针对的数据集和组成部分，概述了流行方法的优缺点，并从选定的文献中报告了详细的方法。电厂的复杂性为有效实施PHM提出了重大挑战。数据驱动技术，特别是机器学习和深度学习，已经成为应对这些挑战的流行解决方案。虽然诊断方法已经取得了实质性的进步，但电厂的预后仍然不发达，需要进一步的研究。本文讨论了与故障诊断和预测相关的挑战，强调解决这些问题可以显著提高PHM的有效性。通过回顾最近的方法进展，总结各种方法的优缺点，并确定关键挑战，本文有助于加深对该领域的理解，并强调未来研究的机会。

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引用次数: 0

An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features 天然气长输管道特征智能识别的集成深度学习模型

IF 9.4 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

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

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.

管道特征识别对天然气长输管道的可靠性和安全性至关重要。利用人工或机器学习方法识别管道特征不仅效率低下，而且存在误判率高、鲁棒性差等问题。为了克服上述问题，本文提出了一种基于多尺度注意卷积神经网络（MACNN）和Gated_Twins_Transformer的管道特征识别方法。利用MACNN提取管道特征的多尺度信息，然后利用注意机制对重要的特征信息进行集中，对不重要的特征信息进行抑制。然后将其传输到Gated_Twins_Transformer模型，该模型使用门控机制和双胞胎编码器模块确定数据长度和输入维数的重要性，并以不同的权重关注两者的特征信息，Transformer增强了全局特征的提取。最后，将实测的管道弯曲应变数据作为模型输入，进行训练和测试，并通过准确度、精度、召回率和F1-score等指标与其他先进模型进行比较，验证本文模型的优越性。

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引用次数: 0